update
This commit is contained in:
DatTT127
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# Stage 1: Build stage
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FROM python:3.12 AS builder
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WORKDIR /app
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# Install dependencies into a virtual environment to make copying easy
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RUN python -m venv /opt/venv
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ENV PATH="/opt/venv/bin:$PATH"
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COPY requirements.txt .
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RUN pip install --no-cache-dir -r requirements.txt
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# Stage 2: Final Runtime stage
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FROM python:3.12-slim
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WORKDIR /app
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# Copy the pre-compiled Python packages from the builder stage
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COPY --from=builder /opt/venv /opt/venv
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COPY . .
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# Ensure the virtual environment is used
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ENV PATH="/opt/venv/bin:$PATH"
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EXPOSE 8000
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CMD ["python", "main.py"]
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from fastapi import FastAPI, File, UploadFile, HTTPException, Query, Request, Response
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from pydantic import BaseModel
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from fastapi.responses import JSONResponse, FileResponse
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from fastapi.staticfiles import StaticFiles
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from fastapi.middleware.cors import CORSMiddleware
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import os
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import uuid
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import numpy as np
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from PIL import Image, ImageDraw
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torchvision.models as models
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from torchvision import transforms
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import uvicorn
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from pathlib import Path
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import cv2
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import io
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import base64
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from datetime import datetime
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import re
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# Import custom models
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import sys
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sys.path.append('.')
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from arch.efficientfeedback import EfficientFeedbackNetwork
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from arch.unet3plus_att import UNet3Plus_Attention
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from pdf_service import generate_medical_report
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# Configuration
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UPLOAD_FOLDER = 'uploads'
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RESULTS_FOLDER = 'results'
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TEMPLATES_FOLDER = 'templates'
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# for folder in [UPLOAD_FOLDER, RESULTS_FOLDER, TEMPLATES_FOLDER]:
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# os.makedirs(folder, exist_ok=True)
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os.makedirs(TEMPLATES_FOLDER, exist_ok=True)
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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# Classes
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ANGLE_CLASSES = ['med-lat', 'post-trans', 'sup-trans-flex', 'sup-up-long']
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SEGMENT_CLASSES_SUPRAPAT = {0: "background", 1: "effusion", 2: "fat", 3: "fat-pat", 4: "femur", 5: "synovium", 6: "tendon"}
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SEGMENT_CLASSES_POST = {0: "background", 1: "fat", 2: "tendon", 3: "muscle", 4: "femur", 5: "artery", 6: "baker's cyst"}
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# Color map for Suprapat
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COLOR_MAP_SUP = {
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'background': {'color': [0, 0, 0], 'name': 'Nền'},
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'effusion': {'color': [255, 0, 0], 'name': 'Dịch khớp'},
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'fat': {'color': [255, 255, 0], 'name': 'Mỡ'},
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'fat-pat': {'color': [0, 255, 255], 'name': 'Mỡ Hoffa'},
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'femur': {'color': [0, 255, 0], 'name': 'Xương đùi'},
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'synovium': {'color': [255, 0, 255], 'name': 'Màng hoạt dịch'},
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'tendon': {'color': [0, 0, 255], 'name': 'Gân'}
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}
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# Color map for Post-trans
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COLOR_MAP_POST = {
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'background': {'color': [0, 0, 0], 'name': 'Nền'},
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"baker's cyst": {'color': [255, 0, 0], 'name': "Baker's cyst"},
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'fat': {'color': [255, 255, 0], 'name': 'Mỡ'},
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'muscle': {'color': [0, 255, 255], 'name': 'Cơ bắp'},
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'femur': {'color': [0, 255, 0], 'name': 'Xương đùi'},
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'synovium': {'color': [255, 0, 255], 'name': 'Màng hoạt dịch'},
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'tendon': {'color': [0, 0, 255], 'name': 'Gân'}
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}
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# Measurement configuration
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DEFAULT_MEASURE_IDS = [1, 5]
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PIXEL_TO_MM = 45.0 / 655.0
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app = FastAPI(title="Medical Image Analysis API")
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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# app.mount("/uploads", StaticFiles(directory=UPLOAD_FOLDER), name="uploads")
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# app.mount("/results", StaticFiles(directory=RESULTS_FOLDER), name="results")
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# ============ MODEL LOADING ============
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def load_angle_model(model_name):
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print(f"📄 Loading angle model: {model_name}")
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if model_name == "convnext":
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model = models.convnext_tiny(weights=None)
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model.classifier[2] = nn.Linear(model.classifier[2].in_features, 4)
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checkpoint = torch.load(f"models/best_convnext_tiny.pth", map_location=device, weights_only=False)
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checkpoint = {k.replace('model.', ''): v for k, v in checkpoint.items()}
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model.load_state_dict(checkpoint)
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elif model_name == "densenet":
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model = models.densenet121(weights=None)
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model.classifier = nn.Linear(model.classifier.in_features, 4)
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checkpoint = torch.load(f"models/best_densenet.pth", map_location=device, weights_only=False)
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checkpoint = {k.replace('model.', ''): v for k, v in checkpoint.items()}
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model.load_state_dict(checkpoint)
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elif model_name == "resnet50":
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model = models.resnet50(weights=None)
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model.fc = nn.Linear(model.fc.in_features, 4)
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checkpoint = torch.load(f"models/best_resnet50.pth", map_location=device, weights_only=False)
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checkpoint = {k.replace('model.', ''): v for k, v in checkpoint.items()}
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model.load_state_dict(checkpoint)
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elif model_name == "efficientnet_b2":
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model = models.efficientnet_b2(weights=None)
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model.classifier[1] = nn.Linear(model.classifier[1].in_features, 4)
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checkpoint = torch.load(f"models/best_efficientnet_b2.pth", map_location=device, weights_only=False)
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checkpoint = {k.replace('model.', ''): v for k, v in checkpoint.items()}
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model.load_state_dict(checkpoint)
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elif model_name == "swin":
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model = models.swin_v2_s(weights=None)
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model.head = nn.Linear(model.head.in_features, 4)
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checkpoint = torch.load(f"models/best_swin_v2_s.pth", map_location=device, weights_only=False)
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checkpoint = {k.replace('model.', ''): v for k, v in checkpoint.items()}
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model.load_state_dict(checkpoint)
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else:
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raise ValueError(f"Unknown angle model: {model_name}")
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print(f"✅ Loaded: {model_name}")
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return model.to(device).eval()
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def load_inflammation_model():
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print("📄 Loading inflammation model")
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model = models.efficientnet_b0(weights=None)
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model.classifier[1] = nn.Linear(model.classifier[1].in_features, 2)
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model.load_state_dict(torch.load("models/efficientnet_b0_ultrasound_2_class.pth", map_location=device, weights_only=False))
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print("✅ Loaded inflammation model")
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return model.to(device).eval()
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def load_segmentation_model_sup(model_name):
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print(f"📄 Loading segmentation model SUP: {model_name}")
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if model_name == "deeplabv3":
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model = models.segmentation.deeplabv3_resnet50(weights=None)
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in_ch = model.classifier[-1].in_channels
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model.classifier = nn.Sequential(
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model.classifier[0],
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nn.Dropout(0.3),
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nn.Conv2d(in_ch, 7, kernel_size=1)
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)
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model.load_state_dict(torch.load("models/best_model_Deeplav3.pth", map_location=device, weights_only=False), strict=False)
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elif model_name == "unet_resnet101":
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try:
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from segmentation_models_pytorch import Unet
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model = Unet(encoder_name="resnet101", encoder_weights=None, classes=7)
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model.load_state_dict(torch.load("models/unet_resnet101.pth", map_location=device, weights_only=False))
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except ImportError:
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raise ValueError("segmentation_models_pytorch not installed")
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elif model_name == "efficientfeedback":
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model = EfficientFeedbackNetwork(in_channels=3, num_class=7)
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model.load_state_dict(torch.load("models/efficientfeedback.pth", map_location=device, weights_only=False))
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elif model_name == "unet3plus":
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model = UNet3Plus_Attention(in_channels=3, num_classes=7)
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model.load_state_dict(torch.load("models/unet3plus_att.pth", map_location=device, weights_only=False))
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else:
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raise ValueError(f"Unknown segmentation model: {model_name}")
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print(f"✅ Loaded SUP: {model_name}")
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return model.to(device).eval()
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def load_segmentation_model_post(model_name):
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print(f"📄 Loading segmentation model POST: {model_name}")
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if model_name == "deeplabv3_resnet101":
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model = models.segmentation.deeplabv3_resnet101(weights=None)
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in_ch = model.classifier[-1].in_channels
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model.classifier = nn.Sequential(
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model.classifier[0],
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nn.Dropout(0.3),
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nn.Conv2d(in_ch, 7, kernel_size=1)
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)
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model.load_state_dict(torch.load("models/best_model_deeplabv3_resnet101_seed_16.pth", map_location=device, weights_only=False), strict=False)
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else:
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raise ValueError(f"Unknown post segmentation model: {model_name}")
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print(f"✅ Loaded POST: {model_name}")
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return model.to(device).eval()
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# Transforms
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angle_transform = transforms.Compose([
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transforms.Resize((224, 224)),
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transforms.ToTensor(),
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transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
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])
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inflammation_transform = transforms.Compose([
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transforms.Resize((224, 224)),
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transforms.ToTensor(),
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transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
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])
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segmentation_transform = transforms.Compose([
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transforms.Resize((512, 512)),
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transforms.ToTensor()
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])
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# ============ PREDICTION ============
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@torch.no_grad()
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def predict_angle(model, image_pil):
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img_tensor = angle_transform(image_pil).unsqueeze(0).to(device)
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output = model(img_tensor)
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probs = torch.softmax(output, dim=1)
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pred_class = torch.argmax(probs, dim=1).item()
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confidence = probs[0][pred_class].item()
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return ANGLE_CLASSES[pred_class], round(confidence * 100, 2)
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@torch.no_grad()
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def predict_inflammation(model, image_pil):
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img_tensor = inflammation_transform(image_pil).unsqueeze(0).to(device)
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output = model(img_tensor)
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probs = torch.softmax(output, dim=1)
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pred_class = torch.argmax(probs, dim=1).item()
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confidence = probs[0][pred_class].item()
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is_inflammation = (pred_class == 1)
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return is_inflammation, round(confidence * 100, 2)
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@torch.no_grad()
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def segment_image(model, image_pil, model_type, angle_type):
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original_size = image_pil.size
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img_tensor = segmentation_transform(image_pil).unsqueeze(0).to(device)
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if model_type.startswith("deeplabv3"):
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outputs = model(img_tensor)['out']
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else:
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outputs = model(img_tensor)
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upsampled = F.interpolate(outputs, size=original_size[::-1], mode='bilinear', align_corners=False)
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preds = upsampled.argmax(dim=1)[0].cpu().numpy()
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if angle_type == 'sup' and model_type in ["unet3plus", "efficientfeedback"]:
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remap = {0: 0, 1: 2, 2: 6, 3: 1, 4: 4, 5: 5, 6: 3}
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preds = np.vectorize(remap.get)(preds)
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class_map = SEGMENT_CLASSES_SUPRAPAT if angle_type == 'sup' else SEGMENT_CLASSES_POST
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masks = {}
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for class_id, class_name in class_map.items():
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mask = (preds == class_id).astype(np.uint8)
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masks[class_name] = mask
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return preds, masks
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def get_mask_bounding_box(mask, dist_percent=0.01):
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"""
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Duyệt toàn bộ vùng được mask, loại bỏ nhiễu và trả về khung bao (Bounding Box).
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Áp dụng quy tắc kết hợp:
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1. Giữ lại khối có diện tích lớn nhất (vùng trung tâm).
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2. Giữ lại các khối phụ nếu thỏa mãn một trong hai điều kiện:
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- Diện tích >= 1/5 diện tích khối lớn nhất.
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- Khoảng cách tới khối lớn nhất <= dist_percent * chiều rộng ảnh.
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"""
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if mask is None or np.sum(mask) == 0:
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return None
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# 1. Chuyển sang uint8
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mask_uint8 = mask.astype(np.uint8)
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if np.max(mask_uint8) == 1:
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mask_uint8 *= 255
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# Lấy chiều rộng ảnh để tính ngưỡng khoảng cách theo %
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img_width = mask_uint8.shape[1]
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dist_threshold = img_width * dist_percent
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# 2. Làm sạch mask cơ bản (Morphological Opening)
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kernel = np.ones((5, 5), np.uint8)
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clean_mask = cv2.morphologyEx(mask_uint8, cv2.MORPH_OPEN, kernel)
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# 3. Tìm các đường bao (các khối tách rời)
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contours, _ = cv2.findContours(clean_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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if not contours:
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return None
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# 4. Tính diện tích từng khối và tìm khối lớn nhất
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contour_info = []
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for cnt in contours:
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contour_info.append({'cnt': cnt, 'area': cv2.contourArea(cnt)})
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# Sắp xếp theo diện tích giảm dần
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contour_info.sort(key=lambda x: x['area'], reverse=True)
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main_block = contour_info[0]
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max_area = main_block['area']
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if max_area < 50:
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return None
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# 5. Chuẩn bị để tính khoảng cách (Distance Transform)
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main_mask = np.zeros_like(mask_uint8)
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cv2.drawContours(main_mask, [main_block['cnt']], -1, 255, -1)
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# dist_map chứa khoảng cách từ mỗi điểm tới biên gần nhất của khối chính
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dist_map = cv2.distanceTransform(255 - main_mask, cv2.DIST_L2, 3)
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# 6. Lọc các khối
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significant_contours = [main_block['cnt']]
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area_threshold = max_area / 4.0
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for i in range(1, len(contour_info)):
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other = contour_info[i]
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# Tạo mask cho khối đang xét để lấy giá trị khoảng cách
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other_mask = np.zeros_like(mask_uint8)
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cv2.drawContours(other_mask, [other['cnt']], -1, 255, -1)
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# Khoảng cách nhỏ nhất từ khối này tới khối chính
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min_dist = np.min(dist_map[other_mask > 0])
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# Điều kiện giữ lại: (Diện tích đủ lớn) HOẶC (Ở gần khối chính theo %)
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if other['area'] >= area_threshold or min_dist <= dist_threshold:
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significant_contours.append(other['cnt'])
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# 7. Tính toán bounding box bao quanh tất cả các vùng được chọn
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all_points = np.concatenate(significant_contours)
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x, y, w, h = cv2.boundingRect(all_points)
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return x, y, w, h
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def find_max_continuous_segment(col_array):
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padded = np.concatenate(([0], col_array, [0]))
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diffs = np.diff(padded)
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starts = np.where(diffs == 1)[0]
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ends = np.where(diffs == -1)[0]
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if len(starts) == 0:
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return 0, -1, -1
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lengths = ends - starts
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max_idx = np.argmax(lengths)
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max_len = lengths[max_idx]
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return max_len, starts[max_idx], ends[max_idx]
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def measure_thickness_new(masks, image_size, measure_ids=None):
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if measure_ids is None:
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measure_ids = DEFAULT_MEASURE_IDS
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width, height = image_size
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mask_all_labels = np.zeros((height, width), dtype=np.uint8)
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mask_measure = np.zeros((height, width), dtype=np.uint8)
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has_any_label = False
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if 'fat-pat' in masks:
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class_map = SEGMENT_CLASSES_SUPRAPAT
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else:
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class_map = SEGMENT_CLASSES_POST
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for class_id, class_name in class_map.items():
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if class_name not in masks or class_name == 'background':
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continue
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mask = masks[class_name]
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if np.sum(mask) > 0:
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has_any_label = True
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mask_all_labels = np.logical_or(mask_all_labels, mask).astype(np.uint8)
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if class_id in measure_ids:
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mask_measure = np.logical_or(mask_measure, mask).astype(np.uint8)
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if not has_any_label or np.sum(mask_measure) == 0:
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return None
|
||||
|
||||
# Đóng khung toàn bộ vùng được mask (xương, màng, dịch, mỡ...)
|
||||
bbox_all = get_mask_bounding_box(mask_all_labels)
|
||||
if bbox_all is None:
|
||||
return None
|
||||
|
||||
x_all, y_all, w_all, h_all = bbox_all
|
||||
|
||||
# Từ khung này, xác định vùng quét là 1/3 ở giữa theo chiều ngang
|
||||
roi_start = x_all + (w_all // 3)
|
||||
roi_end = x_all + (2 * w_all // 3)
|
||||
|
||||
roi_strip = mask_measure[:, roi_start:roi_end]
|
||||
|
||||
global_max_len_px = 0
|
||||
best_x_rel = 0
|
||||
best_y_start = 0
|
||||
best_y_end = 0
|
||||
|
||||
for x in range(roi_strip.shape[1]):
|
||||
col = roi_strip[:, x]
|
||||
if not np.any(col):
|
||||
continue
|
||||
|
||||
length, y_s, y_e = find_max_continuous_segment(col)
|
||||
|
||||
if length > global_max_len_px:
|
||||
global_max_len_px = length
|
||||
best_x_rel = x
|
||||
best_y_start = y_s
|
||||
best_y_end = y_e
|
||||
|
||||
if global_max_len_px == 0:
|
||||
return None
|
||||
|
||||
thickness_mm = global_max_len_px * PIXEL_TO_MM
|
||||
real_x = roi_start + best_x_rel
|
||||
|
||||
print(f"📏 Measurement: {thickness_mm:.2f}mm ({global_max_len_px}px) at x={real_x}")
|
||||
|
||||
return {
|
||||
'thickness_px': int(global_max_len_px),
|
||||
'thickness_mm': float(round(thickness_mm, 2)),
|
||||
'x': int(real_x),
|
||||
'y_start': int(best_y_start),
|
||||
'y_end': int(best_y_end),
|
||||
'roi_start': int(roi_start),
|
||||
'roi_end': int(roi_end),
|
||||
'bbox': {'x': int(x_all), 'y': int(y_all), 'w': int(w_all), 'h': int(h_all)}
|
||||
}
|
||||
|
||||
def analyze_inflammation_severity(masks, image_size):
|
||||
if not masks:
|
||||
return None
|
||||
|
||||
width, height = image_size
|
||||
total_pixels = width * height
|
||||
|
||||
effusion_mask = masks.get('effusion', np.zeros((height, width), dtype=np.uint8))
|
||||
effusion_pixels = int(np.sum(effusion_mask))
|
||||
effusion_ratio = (effusion_pixels / total_pixels) * 100
|
||||
|
||||
effusion_thickness = 0
|
||||
if effusion_pixels > 0:
|
||||
rows_with_effusion = np.any(effusion_mask > 0, axis=1)
|
||||
if np.any(rows_with_effusion):
|
||||
effusion_thickness = int(np.sum(rows_with_effusion))
|
||||
|
||||
synovium_mask = masks.get('synovium', np.zeros((height, width), dtype=np.uint8))
|
||||
synovium_pixels = int(np.sum(synovium_mask))
|
||||
synovium_ratio = (synovium_pixels / total_pixels) * 100
|
||||
|
||||
effusion_score = min(effusion_thickness / height * 100, 100)
|
||||
synovium_score = synovium_ratio
|
||||
combined_score = (effusion_score * 0.6 + synovium_score * 0.4)
|
||||
|
||||
if combined_score > 15:
|
||||
level, severity, color = 3, "Nặng", "#dc3545"
|
||||
description = f"Dịch khớp dày ({effusion_thickness}px), màng hoạt dịch tăng sinh rõ"
|
||||
elif combined_score >= 8:
|
||||
level, severity, color = 2, "Trung bình", "#fd7e14"
|
||||
description = f"Dịch khớp trung bình ({effusion_thickness}px), màng hoạt dịch tăng sinh vừa"
|
||||
elif combined_score >= 3:
|
||||
level, severity, color = 1, "Nhẹ", "#ffc107"
|
||||
description = f"Dịch khớp mỏng ({effusion_thickness}px), màng hoạt dịch tăng sinh nhẹ"
|
||||
else:
|
||||
level, severity, color = 0, "Rất nhẹ", "#28a745"
|
||||
description = "Lượng dịch và màng hoạt dịch trong giới hạn bình thường"
|
||||
|
||||
return {
|
||||
'level': int(level),
|
||||
'severity': severity,
|
||||
'color': color,
|
||||
'description': description,
|
||||
'effusion': {'pixels': effusion_pixels, 'ratio': float(round(effusion_ratio, 2)), 'thickness': effusion_thickness},
|
||||
'synovium': {'pixels': synovium_pixels, 'ratio': float(round(synovium_ratio, 2))},
|
||||
'combined_score': float(round(combined_score, 2))
|
||||
}
|
||||
|
||||
def create_segmentation_overlay(image_pil, masks, measurement=None, angle_type='sup'):
|
||||
if masks is None:
|
||||
return image_pil
|
||||
|
||||
color_map = COLOR_MAP_SUP if angle_type == 'sup' else COLOR_MAP_POST
|
||||
|
||||
img_array = np.array(image_pil)
|
||||
overlay = img_array.copy()
|
||||
|
||||
for class_name, mask in masks.items():
|
||||
if class_name in color_map and np.sum(mask) > 0:
|
||||
color = color_map[class_name]['color']
|
||||
for i in range(3):
|
||||
overlay[:, :, i] = np.where(mask > 0,
|
||||
(overlay[:, :, i] * 0.6 + color[i] * 0.4).astype(np.uint8),
|
||||
overlay[:, :, i])
|
||||
|
||||
overlay_pil = Image.fromarray(overlay)
|
||||
draw = ImageDraw.Draw(overlay_pil)
|
||||
|
||||
for class_name in ['effusion', 'synovium']:
|
||||
mask = masks.get(class_name)
|
||||
if mask is not None and np.sum(mask) > 0:
|
||||
mask_uint8 = (mask * 255).astype(np.uint8)
|
||||
contours, _ = cv2.findContours(mask_uint8, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
|
||||
|
||||
for contour in contours:
|
||||
points = contour.reshape(-1, 2).tolist()
|
||||
if len(points) > 2:
|
||||
points = [(int(p[0]), int(p[1])) for p in points]
|
||||
draw.line(points + [points[0]], fill=(255, 255, 255), width=3)
|
||||
|
||||
if measurement and angle_type == 'sup':
|
||||
x = measurement['x']
|
||||
y_start = measurement['y_start']
|
||||
y_end = measurement['y_end']
|
||||
thickness_mm = measurement['thickness_mm']
|
||||
roi_start = measurement['roi_start']
|
||||
roi_end = measurement['roi_end']
|
||||
bbox = measurement['bbox']
|
||||
|
||||
draw.rectangle(
|
||||
[bbox['x'], bbox['y'], bbox['x'] + bbox['w'], bbox['y'] + bbox['h']],
|
||||
outline=(0, 255, 0), # Chuyển sang xanh lá cho dễ nhìn
|
||||
width=3 # Tăng độ dày khung
|
||||
)
|
||||
|
||||
h = image_pil.size[1]
|
||||
draw.line([(roi_start, 0), (roi_start, h)], fill=(0, 255, 255), width=2)
|
||||
draw.line([(roi_end, 0), (roi_end, h)], fill=(0, 255, 255), width=2)
|
||||
|
||||
draw.line([(x, y_start), (x, y_end)], fill=(255, 0, 0), width=4)
|
||||
|
||||
radius = 4
|
||||
draw.ellipse([x-radius, y_start-radius, x+radius, y_start+radius],
|
||||
fill=(0, 255, 0), outline=(255, 255, 255), width=2)
|
||||
draw.ellipse([x-radius, y_end-radius, x+radius, y_end+radius],
|
||||
fill=(0, 255, 0), outline=(255, 255, 255), width=2)
|
||||
|
||||
text = f"{thickness_mm:.2f} mm"
|
||||
|
||||
try:
|
||||
from PIL import ImageFont
|
||||
font = ImageFont.load_default()
|
||||
bbox_text = draw.textbbox((0, 0), text, font=font)
|
||||
text_w = bbox_text[2] - bbox_text[0]
|
||||
text_h = bbox_text[3] - bbox_text[1]
|
||||
except:
|
||||
text_w, text_h = 100, 20
|
||||
|
||||
text_x = x + 8
|
||||
text_y = y_start - text_h - 8
|
||||
|
||||
draw.rectangle(
|
||||
[text_x - 2, text_y - 2, text_x + text_w + 2, text_y + text_h + 2],
|
||||
fill=(0, 0, 0)
|
||||
)
|
||||
draw.text((text_x, text_y), text, fill=(255, 0, 0))
|
||||
|
||||
return overlay_pil
|
||||
|
||||
def apply_clahe(image_pil):
|
||||
"""Áp dụng thuật toán CLAHE để tăng độ tương phản. Phục vụ cả hiển thị và làm đầu vào AI."""
|
||||
# Chuyển từ PIL sang OpenCV (numpy array)
|
||||
img_array = np.array(image_pil)
|
||||
# Chuyển sang thang độ xám (Gray) để xử lý CLAHE
|
||||
gray = cv2.cvtColor(img_array, cv2.COLOR_RGB2GRAY)
|
||||
|
||||
# Tạo đối tượng CLAHE
|
||||
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
|
||||
enhanced_gray = clahe.apply(gray)
|
||||
|
||||
# Chuyển ngược lại sang RGB (3 kênh) để tương thích với các models
|
||||
enhanced_rgb = cv2.cvtColor(enhanced_gray, cv2.COLOR_GRAY2RGB)
|
||||
return Image.fromarray(enhanced_rgb)
|
||||
|
||||
# Mount thư mục static (CSS, JS)
|
||||
app.mount("/css", StaticFiles(directory="templates/css"), name="css")
|
||||
app.mount("/js", StaticFiles(directory="templates/js"), name="js")
|
||||
|
||||
@app.get("/")
|
||||
async def read_index():
|
||||
html_file = Path(TEMPLATES_FOLDER) / "index.html"
|
||||
if html_file.exists():
|
||||
return FileResponse(html_file)
|
||||
return JSONResponse({"error": "Template not found"})
|
||||
|
||||
@app.post("/api/analyze")
|
||||
async def analyze_image(
|
||||
image: UploadFile = File(...),
|
||||
angle_model: str = Query("convnext"),
|
||||
inflammation_model: str = Query("efficientnet_b0"),
|
||||
segment_model_sup: str = Query("deeplabv3"),
|
||||
segment_model_post: str = Query("deeplabv3_resnet101")
|
||||
):
|
||||
try:
|
||||
print(f"\n{'='*60}")
|
||||
print(f"📊 NEW REQUEST")
|
||||
print(f"Models: angle={angle_model}, inflam={inflammation_model}")
|
||||
print(f" seg_sup={segment_model_sup}, seg_post={segment_model_post}")
|
||||
print(f"{'='*60}")
|
||||
|
||||
contents = await image.read()
|
||||
image_pil = Image.open(io.BytesIO(contents)).convert('RGB')
|
||||
|
||||
# Tạo ảnh tăng cường độ tương phản (Enhanced) cho mục đích hiển thị
|
||||
enhanced_pil = apply_clahe(image_pil)
|
||||
buffered_en = io.BytesIO()
|
||||
enhanced_pil.save(buffered_en, format="PNG")
|
||||
enhanced_str = base64.b64encode(buffered_en.getvalue()).decode()
|
||||
|
||||
result = {
|
||||
'success': True,
|
||||
'filename': image.filename,
|
||||
'images': {
|
||||
'enhanced': f"data:image/png;base64,{enhanced_str}"
|
||||
},
|
||||
'models_used': {
|
||||
'angle': angle_model,
|
||||
'inflammation': inflammation_model,
|
||||
'segmentation_sup': segment_model_sup,
|
||||
'segmentation_post': segment_model_post
|
||||
}
|
||||
}
|
||||
|
||||
angle_clf = load_angle_model(angle_model)
|
||||
angle, angle_conf = predict_angle(angle_clf, image_pil)
|
||||
result['angle'] = {'class': angle, 'confidence': angle_conf}
|
||||
print(f"✅ Angle: {angle} ({angle_conf}%)")
|
||||
|
||||
if 'post-trans' in angle.lower():
|
||||
print(f"🔍 POST-TRANS pipeline")
|
||||
|
||||
inflam_model = load_inflammation_model()
|
||||
has_inflammation, inflam_conf = predict_inflammation(inflam_model, image_pil)
|
||||
result['inflammation'] = {'detected': has_inflammation, 'confidence': inflam_conf}
|
||||
print(f"✅ Inflammation: {has_inflammation} ({inflam_conf}%)")
|
||||
|
||||
if has_inflammation:
|
||||
seg_model = load_segmentation_model_post(segment_model_post)
|
||||
preds, masks = segment_image(seg_model, image_pil, segment_model_post, 'post')
|
||||
|
||||
if masks:
|
||||
segmented_img = create_segmentation_overlay(image_pil, masks, None, 'post')
|
||||
# Convert to base64
|
||||
buffered = io.BytesIO()
|
||||
segmented_img.save(buffered, format="PNG")
|
||||
img_str = base64.b64encode(buffered.getvalue()).decode()
|
||||
|
||||
result['images']['segmented'] = f"data:image/png;base64,{img_str}"
|
||||
|
||||
detected_classes = [k for k, v in masks.items() if np.sum(v) > 0]
|
||||
color_legend = []
|
||||
for class_name in detected_classes:
|
||||
if class_name in COLOR_MAP_POST:
|
||||
color_legend.append({
|
||||
'name': COLOR_MAP_POST[class_name]['name'],
|
||||
'color': COLOR_MAP_POST[class_name]['color'],
|
||||
'key': class_name
|
||||
})
|
||||
|
||||
result['segmentation'] = {
|
||||
'performed': True,
|
||||
'classes_detected': detected_classes,
|
||||
'color_legend': color_legend,
|
||||
'angle_type': 'post'
|
||||
}
|
||||
|
||||
print(f"✅ Segmentation POST completed")
|
||||
else:
|
||||
print(f"ℹ️ No inflammation detected - skipping segmentation POST")
|
||||
|
||||
elif 'sup-up-long' in angle.lower():
|
||||
print(f"🔍 SUPRAPAT pipeline")
|
||||
|
||||
inflam_model = load_inflammation_model()
|
||||
has_inflammation, inflam_conf = predict_inflammation(inflam_model, image_pil)
|
||||
result['inflammation'] = {'detected': has_inflammation, 'confidence': inflam_conf}
|
||||
print(f"✅ Inflammation: {has_inflammation} ({inflam_conf}%)")
|
||||
|
||||
if has_inflammation:
|
||||
seg_model = load_segmentation_model_sup(segment_model_sup)
|
||||
preds, masks = segment_image(seg_model, image_pil, segment_model_sup, 'sup')
|
||||
|
||||
if masks:
|
||||
measurement = measure_thickness_new(masks, image_pil.size)
|
||||
|
||||
segmented_img = create_segmentation_overlay(image_pil, masks, measurement, 'sup')
|
||||
# Convert to base64
|
||||
buffered = io.BytesIO()
|
||||
segmented_img.save(buffered, format="PNG")
|
||||
img_str = base64.b64encode(buffered.getvalue()).decode()
|
||||
|
||||
result['images']['segmented'] = f"data:image/png;base64,{img_str}"
|
||||
|
||||
if measurement:
|
||||
result['measurement'] = {
|
||||
'thickness_mm': measurement['thickness_mm'],
|
||||
'thickness_px': measurement['thickness_px'],
|
||||
'location_x': measurement['x'],
|
||||
'y_start': measurement['y_start'],
|
||||
'y_end': measurement['y_end']
|
||||
}
|
||||
print(f"✅ Measurement: {measurement['thickness_mm']:.2f}mm at x={measurement['x']}")
|
||||
|
||||
detected_classes = [k for k, v in masks.items() if np.sum(v) > 0]
|
||||
color_legend = []
|
||||
for class_name in detected_classes:
|
||||
if class_name in COLOR_MAP_SUP:
|
||||
color_legend.append({
|
||||
'name': COLOR_MAP_SUP[class_name]['name'],
|
||||
'color': COLOR_MAP_SUP[class_name]['color'],
|
||||
'key': class_name
|
||||
})
|
||||
|
||||
result['segmentation'] = {
|
||||
'performed': True,
|
||||
'classes_detected': detected_classes,
|
||||
'color_legend': color_legend,
|
||||
'angle_type': 'sup'
|
||||
}
|
||||
|
||||
severity = analyze_inflammation_severity(masks, image_pil.size)
|
||||
if severity:
|
||||
result['severity'] = severity
|
||||
print(f"✅ Severity: {severity['severity']}")
|
||||
|
||||
print(f"✅ Segmentation SUP completed")
|
||||
else:
|
||||
print(f"ℹ️ No inflammation detected - skipping segmentation SUP")
|
||||
|
||||
else:
|
||||
print(f"ℹ️ Other angle - only angle classification")
|
||||
|
||||
print(f"{'='*60}\n")
|
||||
return JSONResponse(result)
|
||||
|
||||
except Exception as e:
|
||||
import traceback
|
||||
print(f"❌ Error: {e}")
|
||||
print(traceback.format_exc())
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@app.get("/api/health")
|
||||
async def health_check():
|
||||
return JSONResponse({'status': 'healthy'})
|
||||
|
||||
def sanitize_name(name):
|
||||
# Loại bỏ ký tự không hợp lệ cho folder trên Windows
|
||||
if not name: return "unknown"
|
||||
# Thay thế ký tự lạ bằng dấu gạch dưới
|
||||
clean = re.sub(r'[\\/*?:"<>|]', "_", name)
|
||||
# Loại bỏ dấu cách thừa
|
||||
clean = clean.strip().replace(" ", "_")
|
||||
return clean
|
||||
|
||||
class SaveDataRequest(BaseModel):
|
||||
patient_info: dict
|
||||
analysis_result: dict
|
||||
images: dict
|
||||
|
||||
@app.post("/api/save")
|
||||
async def save_patient_data(data: SaveDataRequest):
|
||||
try:
|
||||
p = data.patient_info
|
||||
res = data.analysis_result
|
||||
imgs = data.images
|
||||
|
||||
# 1. Tạo thư mục theo mã bệnh nhân (nhóm chính)
|
||||
patient_id = sanitize_name(p.get('id', 'unknown'))
|
||||
patient_name = sanitize_name(p.get('name', 'no_name'))
|
||||
|
||||
# Thư mục chính của bệnh nhân
|
||||
patient_folder = f"{patient_id}_{patient_name}"
|
||||
|
||||
# Thư mục con theo thời gian hiện tại
|
||||
timestamp_folder = datetime.now().strftime("%Y%m%d_%H%M%S")
|
||||
|
||||
# Tổng hợp đường dẫn: patients/ID_Name/TIMESTAMP/
|
||||
target_dir = os.path.join("patients", patient_folder, timestamp_folder)
|
||||
os.makedirs(target_dir, exist_ok=True)
|
||||
|
||||
# 2. Lưu info.txt
|
||||
info_path = os.path.join(target_dir, "info.txt")
|
||||
with open(info_path, "w", encoding="utf-8") as f:
|
||||
f.write(f"--- THÔNG TIN BỆNH NHÂN ---\n")
|
||||
f.write(f"Mã BN: {patient_id}\n")
|
||||
f.write(f"Họ tên: {patient_name}\n")
|
||||
f.write(f"Giới tính: {p.get('gender')}\n")
|
||||
f.write(f"Tuổi: {p.get('age')}\n")
|
||||
f.write(f"Chẩn đoán BS: {p.get('diagnosis')}\n\n")
|
||||
|
||||
f.write(f"--- KẾT QUẢ PHÂN TÍCH AI ---\n")
|
||||
f.write(f"Góc chụp: {res.get('angle', {}).get('class')} ({res.get('angle', {}).get('confidence')}%)\n")
|
||||
|
||||
if 'inflammation' in res:
|
||||
infl = res['inflammation']
|
||||
f.write(f"Viêm nhiễm: {'Có' if infl['detected'] else 'Không'} ({infl['confidence']}%)\n")
|
||||
|
||||
if 'measurement' in res:
|
||||
m = res['measurement']
|
||||
f.write(f"Độ dày màng: {m['thickness_mm']} mm ({m['thickness_px']} px)\n")
|
||||
f.write(f"Vị trí x: {m['location_x']}\n")
|
||||
|
||||
if 'severity' in res:
|
||||
s = res['severity']
|
||||
f.write(f"Mức độ: {s['severity']}\n")
|
||||
f.write(f"Mô tả: {s['description']}\n")
|
||||
|
||||
# 3. Lưu ảnh
|
||||
def save_base64_img(b64_str, filename):
|
||||
if not b64_str: return
|
||||
# Remove header if present
|
||||
if "," in b64_str:
|
||||
b64_str = b64_str.split(",")[1]
|
||||
|
||||
img_data = base64.b64decode(b64_str)
|
||||
with open(os.path.join(target_dir, filename), "wb") as f:
|
||||
f.write(img_data)
|
||||
|
||||
save_base64_img(imgs.get('original'), "original.png")
|
||||
save_base64_img(imgs.get('segmented'), "segmented.png")
|
||||
|
||||
# 4. Tự động lưu PDF báo cáo
|
||||
try:
|
||||
pdf_bytes = generate_medical_report(p, res, imgs)
|
||||
pdf_path = os.path.join(target_dir, "report.pdf")
|
||||
with open(pdf_path, "wb") as f:
|
||||
f.write(bytes(pdf_bytes))
|
||||
print(f"📄 Report PDF saved to: {pdf_path}")
|
||||
except Exception as pdf_err:
|
||||
print(f"⚠️ Warning: Could not auto-save PDF: {pdf_err}")
|
||||
|
||||
print(f"✅ Data saved for patient: {patient_id}")
|
||||
return {"success": True, "folder": target_dir}
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Save Error: {e}")
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@app.post("/api/export-pdf")
|
||||
async def export_patient_pdf(data: SaveDataRequest):
|
||||
try:
|
||||
pdf_bytes = generate_medical_report(
|
||||
data.patient_info,
|
||||
data.analysis_result,
|
||||
data.images
|
||||
)
|
||||
|
||||
filename = f"Phieu_Kham_{sanitize_name(data.patient_info.get('id', 'unknown'))}.pdf"
|
||||
|
||||
return Response(
|
||||
content=bytes(pdf_bytes),
|
||||
media_type="application/pdf",
|
||||
headers={"Content-Disposition": f"attachment; filename={filename}"}
|
||||
)
|
||||
except Exception as e:
|
||||
import traceback
|
||||
print(f"❌ PDF Export Error: {e}")
|
||||
print(traceback.format_exc())
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
if __name__ == '__main__':
|
||||
print("Medical Image Analysis Server")
|
||||
print(f"URL: http://127.0.0.1:8000")
|
||||
print(f"Device: {device}")
|
||||
uvicorn.run(app, host="127.0.0.1", port=8000)
|
||||
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
|
After Width: | Height: | Size: 71 KiB |
@@ -0,0 +1,247 @@
|
||||
from fpdf import FPDF
|
||||
import io
|
||||
import base64
|
||||
import os
|
||||
from datetime import datetime
|
||||
from PIL import Image
|
||||
|
||||
class MedicalReportPDF(FPDF):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.main_font = 'helvetica' # Default fallback
|
||||
self.set_margins(10, 10, 10)
|
||||
|
||||
def header(self):
|
||||
# Logo support
|
||||
logo_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'assets', 'logo.png')
|
||||
show_logo = False
|
||||
|
||||
if os.path.exists(logo_path):
|
||||
logo_stream = get_clean_image_stream(logo_path)
|
||||
if logo_stream:
|
||||
try:
|
||||
self.image(logo_stream, 10, 8, 25, type='PNG')
|
||||
self.set_x(40)
|
||||
show_logo = True
|
||||
except:
|
||||
pass
|
||||
|
||||
# Header with Unicode support
|
||||
try:
|
||||
self.set_font(self.main_font, 'B', 16)
|
||||
title = 'TRUNG TÂM CHẨN ĐOÁN HÌNH ẢNH VKIST'
|
||||
if show_logo:
|
||||
self.cell(0, 10, title, 0, 1, 'L')
|
||||
self.set_x(40)
|
||||
self.set_font(self.main_font, '', 10)
|
||||
self.cell(0, 5, 'Địa chỉ: Khu Công nghệ cao Hòa Lạc, Thạch Thất, Hà Nội', 0, 1, 'L')
|
||||
else:
|
||||
self.cell(0, 10, title, 0, 1, 'C')
|
||||
self.set_font(self.main_font, '', 10)
|
||||
self.cell(0, 10, 'Địa chỉ: Khu Công nghệ cao Hòa Lạc, Thạch Thất, Hà Nội', 0, 1, 'C')
|
||||
self.ln(10)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
def footer(self):
|
||||
self.set_y(-15)
|
||||
try:
|
||||
self.set_font(self.main_font, 'I', 8)
|
||||
self.cell(0, 10, f'Trang {self.page_no()}/{{nb}}', 0, 0, 'C')
|
||||
except Exception:
|
||||
self.set_font('helvetica', 'I', 8)
|
||||
self.cell(0, 10, f'Page {self.page_no()}/{{nb}}', 0, 0, 'C')
|
||||
|
||||
def get_clean_image_stream(image_source):
|
||||
"""
|
||||
Mở ảnh (file path hoặc base64), loại bỏ interlacing và trả về BytesIO stream.
|
||||
Giúp tránh lỗi 'Interlacing not supported' trong FPDF2.
|
||||
"""
|
||||
if not image_source:
|
||||
return None
|
||||
|
||||
try:
|
||||
if isinstance(image_source, str) and image_source.startswith('data:image'):
|
||||
# Xử lý base64
|
||||
header, content = image_source.split(',') if ',' in image_source else (None, image_source)
|
||||
img_data = base64.b64decode(content)
|
||||
img_io = io.BytesIO(img_data)
|
||||
elif isinstance(image_source, str) and os.path.exists(image_source):
|
||||
# Xử lý file path
|
||||
img_io = image_source
|
||||
else:
|
||||
return None
|
||||
|
||||
with Image.open(img_io) as img:
|
||||
# Chuyển đổi sang RGB nếu cần và lưu lại không có interlacing
|
||||
if img.mode in ('RGBA', 'P'):
|
||||
img = img.convert('RGB')
|
||||
|
||||
output = io.BytesIO()
|
||||
img.save(output, format='PNG', optimize=False, interlaced=False)
|
||||
output.seek(0)
|
||||
return output
|
||||
except Exception as e:
|
||||
print(f"⚠️ Lỗi xử lý ảnh: {e}")
|
||||
return None
|
||||
|
||||
def generate_medical_report(patient_info, analysis_result, images_base64):
|
||||
pdf = MedicalReportPDF()
|
||||
|
||||
# Sử dụng đường dẫn tuyệt đối để ổn định
|
||||
base_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
font_dir = os.path.join(base_dir, 'assets', 'fonts')
|
||||
|
||||
# 1. Đăng ký Font nội bộ (Arial đã sao chép)
|
||||
try:
|
||||
pdf.add_font('arial_local', '', os.path.join(font_dir, 'arial.ttf'))
|
||||
pdf.add_font('arial_local', 'B', os.path.join(font_dir, 'arialbd.ttf'))
|
||||
pdf.add_font('arial_local', 'I', os.path.join(font_dir, 'ariali.ttf'))
|
||||
pdf.main_font = 'arial_local'
|
||||
except Exception as e:
|
||||
print(f"Warning: Could not load local Arial fonts: {e}")
|
||||
# Dự phòng cuối cùng
|
||||
pdf.main_font = 'helvetica'
|
||||
|
||||
font_main = pdf.main_font
|
||||
pdf.set_auto_page_break(auto=True, margin=15)
|
||||
pdf.add_page()
|
||||
pdf.alias_nb_pages()
|
||||
|
||||
# Tiêu đề
|
||||
pdf.set_font(font_main, 'B', 14)
|
||||
pdf.cell(0, 10, 'PHIẾU KẾT QUẢ SIÊU ÂM KHỚP GỐI', 0, 1, 'C')
|
||||
pdf.ln(5)
|
||||
|
||||
# Thông tin bệnh nhân
|
||||
pdf.set_x(10)
|
||||
pdf.set_font(font_main, 'B', 11)
|
||||
pdf.cell(0, 8, 'I. THÔNG TIN BỆNH NHÂN', 0, 1, 'L')
|
||||
pdf.set_font(font_main, '', 11)
|
||||
|
||||
# Độ rộng cột an toàn (Tổng 180mm < 190mm khả dụng cho A4)
|
||||
col1 = 95
|
||||
col2 = 85
|
||||
|
||||
pdf.cell(col1, 8, f"Họ tên: {patient_info.get('name', 'N/A')}", 0, 0)
|
||||
pdf.cell(col2, 8, f"Mã BN: {patient_info.get('id', 'N/A')}", 0, 1)
|
||||
|
||||
pdf.cell(col1, 8, f"Giới tính: {patient_info.get('gender', 'N/A')}", 0, 0)
|
||||
pdf.cell(col2, 8, f"Tuổi: {patient_info.get('age', 'N/A')}", 0, 1)
|
||||
|
||||
pdf.ln(5)
|
||||
pdf.line(10, pdf.get_y(), 200, pdf.get_y())
|
||||
pdf.ln(5)
|
||||
|
||||
# Images Section
|
||||
pdf.set_x(10)
|
||||
pdf.set_font(font_main, 'B', 11)
|
||||
pdf.cell(0, 10, 'II. HÌNH ẢNH SIÊU ÂM', 0, 1, 'L')
|
||||
|
||||
y_before_images = pdf.get_y()
|
||||
img_w = 90
|
||||
margin = 5
|
||||
|
||||
# Process Images
|
||||
has_orig = images_base64.get('original')
|
||||
has_seg = images_base64.get('segmented')
|
||||
|
||||
max_img_h = 0
|
||||
|
||||
if has_orig:
|
||||
try:
|
||||
orig_stream = get_clean_image_stream(has_orig)
|
||||
if orig_stream:
|
||||
with Image.open(orig_stream) as img:
|
||||
w, h = img.size
|
||||
img_h = (img_w * h) / w
|
||||
max_img_h = max(max_img_h, img_h)
|
||||
|
||||
orig_stream.seek(0)
|
||||
pdf.image(orig_stream, x=10, y=y_before_images, w=img_w, type='PNG')
|
||||
|
||||
# Label
|
||||
pdf.set_xy(10, y_before_images + img_h + 2)
|
||||
pdf.set_font(font_main, 'I', 9)
|
||||
pdf.cell(img_w, 5, 'Hình 1: Ảnh gốc / Tăng cường', 0, 0, 'C')
|
||||
except Exception as e:
|
||||
print(f"Error processing original image: {e}")
|
||||
|
||||
if has_seg:
|
||||
try:
|
||||
seg_stream = get_clean_image_stream(has_seg)
|
||||
if seg_stream:
|
||||
with Image.open(seg_stream) as img:
|
||||
w, h = img.size
|
||||
img_h = (img_w * h) / w
|
||||
max_img_h = max(max_img_h, img_h)
|
||||
|
||||
seg_stream.seek(0)
|
||||
pdf.image(seg_stream, x=110, y=y_before_images, w=img_w, type='PNG')
|
||||
|
||||
# Label
|
||||
pdf.set_xy(110, y_before_images + img_h + 2)
|
||||
pdf.set_font(font_main, 'I', 9)
|
||||
pdf.cell(img_w, 5, 'Hình 2: Ảnh phân đoạn AI', 0, 1, 'C')
|
||||
except Exception as e:
|
||||
print(f"Error processing segmented image: {e}")
|
||||
|
||||
# Reset Y to after images
|
||||
pdf.set_y(y_before_images + max_img_h + 10)
|
||||
pdf.set_x(10)
|
||||
|
||||
# AI Results
|
||||
pdf.set_font(font_main, 'B', 11)
|
||||
pdf.cell(0, 10, 'III. KẾT QUẢ PHÂN TÍCH TỰ ĐỘNG (AI)', 0, 1, 'L')
|
||||
pdf.set_font(font_main, '', 10)
|
||||
|
||||
angle = analysis_result.get('angle', {})
|
||||
pdf.multi_cell(185, 6, f"• Góc chụp dự đoán: {angle.get('class', 'N/A')} (Độ tin cậy: {angle.get('confidence', 'N/A')}%)")
|
||||
|
||||
if 'inflammation' in analysis_result:
|
||||
infl = analysis_result['inflammation']
|
||||
status = "Có khả năng viêm / Theo dõi viêm" if infl.get('detected') else "Không thấy dấu hiệu viêm rõ rệt"
|
||||
pdf.set_x(10)
|
||||
pdf.multi_cell(185, 6, f"• Tình trạng viêm: {status} (Độ tin cậy: {infl.get('confidence', 'N/A')}%)")
|
||||
|
||||
if 'measurement' in analysis_result:
|
||||
m = analysis_result['measurement']
|
||||
pdf.set_x(10)
|
||||
pdf.multi_cell(185, 6, f"• Đo đạc: Độ dày dịch & màng hoạt dịch đạt mức {m.get('thickness_mm', 'N/A')} mm")
|
||||
|
||||
if 'severity' in analysis_result:
|
||||
s = analysis_result['severity']
|
||||
pdf.set_x(10)
|
||||
pdf.multi_cell(185, 6, f"• Mức độ viêm: {s.get('severity', 'N/A')}")
|
||||
pdf.set_x(10)
|
||||
pdf.set_font(font_main, 'I', 10)
|
||||
pdf.multi_cell(185, 6, f" Chi tiết: {s.get('description', 'N/A')}")
|
||||
pdf.set_font(font_main, '', 10)
|
||||
|
||||
pdf.ln(5)
|
||||
pdf.set_x(10)
|
||||
|
||||
# Doctor Diagnosis
|
||||
pdf.set_font(font_main, 'B', 11)
|
||||
pdf.cell(0, 10, 'IV. CHẨN ĐOÁN VÀ KẾT LUẬN CỦA BÁC SĨ', 0, 1, 'L')
|
||||
pdf.set_font(font_main, '', 11)
|
||||
|
||||
diagnosis = patient_info.get('diagnosis', 'Ghi chú chẩn đoán trống.')
|
||||
pdf.set_x(10)
|
||||
pdf.multi_cell(185, 7, diagnosis)
|
||||
|
||||
pdf.ln(15)
|
||||
|
||||
# Signature
|
||||
current_date = datetime.now()
|
||||
date_str = f"Ngày {current_date.day} tháng {current_date.month} năm {current_date.year}"
|
||||
|
||||
pdf.set_font(font_main, 'I', 11)
|
||||
pdf.cell(0, 8, date_str, 0, 1, 'R')
|
||||
pdf.set_font(font_main, 'B', 11)
|
||||
pdf.cell(0, 8, 'BÁC SĨ CHẨN ĐOÁN', 0, 1, 'R')
|
||||
pdf.ln(15)
|
||||
pdf.set_font(font_main, '', 10)
|
||||
pdf.cell(0, 8, '(Ký và ghi rõ họ tên)', 0, 1, 'R')
|
||||
|
||||
return pdf.output()
|
||||
@@ -0,0 +1,681 @@
|
||||
* { margin: 0; padding: 0; box-sizing: border-box; }
|
||||
body {
|
||||
font-family: 'Segoe UI', Tahoma, sans-serif;
|
||||
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
|
||||
min-height: 100vh;
|
||||
padding: 20px;
|
||||
}
|
||||
.container {
|
||||
max-width: 1600px;
|
||||
margin: 0 auto;
|
||||
background: white;
|
||||
border-radius: 20px;
|
||||
box-shadow: 0 20px 40px rgba(0,0,0,0.1);
|
||||
overflow: hidden;
|
||||
}
|
||||
.header {
|
||||
background: linear-gradient(135deg, #2c3e50 0%, #3498db 100%);
|
||||
color: white;
|
||||
padding: 30px;
|
||||
text-align: center;
|
||||
}
|
||||
.header h1 { font-size: 2rem; margin-bottom: 10px; }
|
||||
.main-content {
|
||||
display: grid;
|
||||
grid-template-columns: 300px 1fr 1fr;
|
||||
gap: 25px;
|
||||
padding: 30px;
|
||||
min-height: 600px;
|
||||
}
|
||||
|
||||
.models-panel {
|
||||
background: #f8f9fa;
|
||||
padding: 20px;
|
||||
border-radius: 15px;
|
||||
height: fit-content;
|
||||
}
|
||||
.models-panel h3 {
|
||||
margin-bottom: 15px;
|
||||
color: #2c3e50;
|
||||
font-size: 1.1rem;
|
||||
}
|
||||
.model-section {
|
||||
background: white;
|
||||
padding: 15px;
|
||||
margin-bottom: 15px;
|
||||
border-radius: 10px;
|
||||
border-left: 4px solid #3498db;
|
||||
}
|
||||
.model-section h4 {
|
||||
font-size: 0.9rem;
|
||||
color: #2c3e50;
|
||||
margin-bottom: 10px;
|
||||
font-weight: 600;
|
||||
}
|
||||
.model-section select {
|
||||
width: 100%;
|
||||
padding: 8px;
|
||||
border: 2px solid #e0e0e0;
|
||||
border-radius: 6px;
|
||||
font-size: 0.85rem;
|
||||
cursor: pointer;
|
||||
}
|
||||
.model-section select:focus {
|
||||
outline: none;
|
||||
border-color: #3498db;
|
||||
}
|
||||
.model-section small {
|
||||
display: block;
|
||||
margin-top: 6px;
|
||||
color: #7f8c8d;
|
||||
font-size: 0.75rem;
|
||||
line-height: 1.3;
|
||||
}
|
||||
|
||||
.upload-panel {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 20px;
|
||||
}
|
||||
.panel-title {
|
||||
font-size: 1.3rem;
|
||||
color: #2c3e50;
|
||||
font-weight: 600;
|
||||
margin-bottom: 10px;
|
||||
}
|
||||
.upload-area {
|
||||
border: 3px dashed #bdc3c7;
|
||||
border-radius: 15px;
|
||||
padding: 40px 20px;
|
||||
text-align: center;
|
||||
cursor: pointer;
|
||||
transition: all 0.3s ease;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
min-height: 250px;
|
||||
}
|
||||
.upload-area:hover {
|
||||
border-color: #3498db;
|
||||
background: #f8f9fa;
|
||||
}
|
||||
.upload-icon { font-size: 3rem; margin-bottom: 15px; }
|
||||
.upload-btn {
|
||||
background: linear-gradient(135deg, #3498db, #2980b9);
|
||||
color: white;
|
||||
padding: 12px 30px;
|
||||
border: none;
|
||||
border-radius: 25px;
|
||||
font-size: 1rem;
|
||||
cursor: pointer;
|
||||
font-weight: 600;
|
||||
transition: all 0.3s ease;
|
||||
}
|
||||
.upload-btn:hover {
|
||||
transform: translateY(-2px);
|
||||
box-shadow: 0 5px 15px rgba(52, 152, 219, 0.4);
|
||||
}
|
||||
.image-preview-box {
|
||||
background: #f8f9fa;
|
||||
border-radius: 15px;
|
||||
padding: 15px;
|
||||
margin-top: 15px;
|
||||
}
|
||||
.image-preview-box h3 {
|
||||
margin-bottom: 12px;
|
||||
color: #2c3e50;
|
||||
font-size: 1.05rem;
|
||||
font-weight: 600;
|
||||
}
|
||||
.preview-image {
|
||||
width: 100%;
|
||||
border-radius: 10px;
|
||||
display: block;
|
||||
}
|
||||
|
||||
.results-panel {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 20px;
|
||||
}
|
||||
|
||||
.angle-result-card {
|
||||
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
|
||||
color: white;
|
||||
padding: 20px;
|
||||
border-radius: 15px;
|
||||
text-align: center;
|
||||
box-shadow: 0 4px 15px rgba(102, 126, 234, 0.3);
|
||||
}
|
||||
.angle-result-card h3 {
|
||||
font-size: 0.9rem;
|
||||
margin-bottom: 10px;
|
||||
opacity: 0.9;
|
||||
}
|
||||
.angle-value {
|
||||
font-size: 2rem;
|
||||
font-weight: 900;
|
||||
margin: 10px 0;
|
||||
text-shadow: 2px 2px 4px rgba(0,0,0,0.3);
|
||||
}
|
||||
.angle-confidence {
|
||||
font-size: 0.85rem;
|
||||
opacity: 0.8;
|
||||
}
|
||||
|
||||
.result-image-box {
|
||||
background: #f8f9fa;
|
||||
border-radius: 15px;
|
||||
padding: 15px;
|
||||
}
|
||||
.result-image-box h3 {
|
||||
margin-bottom: 12px;
|
||||
color: #2c3e50;
|
||||
font-size: 1.05rem;
|
||||
font-weight: 600;
|
||||
}
|
||||
.result-image-box img {
|
||||
width: 100%;
|
||||
border-radius: 10px;
|
||||
}
|
||||
|
||||
.color-legend {
|
||||
background: white;
|
||||
padding: 12px;
|
||||
border-radius: 8px;
|
||||
margin-top: 12px;
|
||||
}
|
||||
.color-legend h4 {
|
||||
font-size: 0.9rem;
|
||||
color: #2c3e50;
|
||||
margin-bottom: 8px;
|
||||
}
|
||||
.legend-items {
|
||||
display: grid;
|
||||
grid-template-columns: 1fr 1fr;
|
||||
gap: 8px;
|
||||
}
|
||||
.legend-item {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 8px;
|
||||
font-size: 0.85rem;
|
||||
}
|
||||
.legend-color {
|
||||
width: 20px;
|
||||
height: 20px;
|
||||
border-radius: 4px;
|
||||
border: 1px solid #ddd;
|
||||
}
|
||||
.legend-highlight {
|
||||
border: 2px solid #fff;
|
||||
box-shadow: 0 0 0 2px #333;
|
||||
}
|
||||
|
||||
.results-grid {
|
||||
display: grid;
|
||||
grid-template-columns: 1fr 1fr;
|
||||
gap: 12px;
|
||||
}
|
||||
.result-card {
|
||||
background: #f8f9fa;
|
||||
border-radius: 12px;
|
||||
padding: 15px;
|
||||
border-left: 5px solid #3498db;
|
||||
}
|
||||
.result-card.full-width {
|
||||
grid-column: 1 / -1;
|
||||
}
|
||||
.result-card h4 {
|
||||
margin-bottom: 10px;
|
||||
color: #2c3e50;
|
||||
font-size: 1rem;
|
||||
}
|
||||
.result-value {
|
||||
font-size: 1.1rem;
|
||||
font-weight: 600;
|
||||
color: #2c3e50;
|
||||
}
|
||||
|
||||
/* Patient Form Styles */
|
||||
.patient-panel {
|
||||
background: #f8f9fa;
|
||||
padding: 20px;
|
||||
border-radius: 15px;
|
||||
margin-bottom: 20px;
|
||||
}
|
||||
.form-grid {
|
||||
display: grid;
|
||||
grid-template-columns: 1fr 1fr;
|
||||
gap: 15px;
|
||||
margin-top: 15px;
|
||||
}
|
||||
.full-width {
|
||||
grid-column: 1 / -1;
|
||||
}
|
||||
.form-group {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 5px;
|
||||
}
|
||||
.form-group label {
|
||||
font-size: 0.85rem;
|
||||
font-weight: 600;
|
||||
color: #34495e;
|
||||
}
|
||||
.form-group label span {
|
||||
color: #e74c3c;
|
||||
}
|
||||
.form-group input, .form-group select, .form-group textarea {
|
||||
padding: 10px;
|
||||
border: 2px solid #e0e0e0;
|
||||
border-radius: 8px;
|
||||
font-size: 0.9rem;
|
||||
}
|
||||
.form-group input:focus, .form-group select:focus, .form-group textarea:focus {
|
||||
outline: none;
|
||||
border-color: #3498db;
|
||||
}
|
||||
.save-section {
|
||||
margin-top: 25px;
|
||||
padding-top: 20px;
|
||||
border-top: 2px solid #eee;
|
||||
display: flex;
|
||||
justify-content: center;
|
||||
gap: 15px;
|
||||
flex-wrap: wrap;
|
||||
}
|
||||
.save-btn {
|
||||
background: linear-gradient(135deg, #2ecc71, #27ae60);
|
||||
color: white;
|
||||
padding: 14px 40px;
|
||||
border: none;
|
||||
border-radius: 30px;
|
||||
font-size: 1.1rem;
|
||||
font-weight: 700;
|
||||
cursor: pointer;
|
||||
transition: all 0.3s ease;
|
||||
display: inline-flex;
|
||||
align-items: center;
|
||||
gap: 10px;
|
||||
}
|
||||
.save-btn:hover:not(:disabled) {
|
||||
transform: translateY(-2px);
|
||||
box-shadow: 0 5px 15px rgba(46, 204, 113, 0.4);
|
||||
}
|
||||
.save-btn:disabled {
|
||||
background: #bdc3c7;
|
||||
cursor: not-allowed;
|
||||
opacity: 0.7;
|
||||
}
|
||||
.save-btn .icon { font-size: 1.2rem; }
|
||||
|
||||
.export-btn {
|
||||
background: linear-gradient(135deg, #3498db, #2980b9);
|
||||
color: white;
|
||||
padding: 14px 40px;
|
||||
border: none;
|
||||
border-radius: 30px;
|
||||
font-size: 1.1rem;
|
||||
font-weight: 700;
|
||||
cursor: pointer;
|
||||
transition: all 0.3s ease;
|
||||
display: inline-flex;
|
||||
align-items: center;
|
||||
gap: 10px;
|
||||
}
|
||||
.export-btn:hover:not(:disabled) {
|
||||
transform: translateY(-2px);
|
||||
box-shadow: 0 5px 15px rgba(52, 152, 219, 0.4);
|
||||
}
|
||||
.export-btn:disabled {
|
||||
background: #bdc3c7;
|
||||
cursor: not-allowed;
|
||||
opacity: 0.7;
|
||||
}
|
||||
.export-btn .icon { font-size: 1.2rem; }
|
||||
.confidence {
|
||||
font-size: 0.85rem;
|
||||
color: #7f8c8d;
|
||||
margin-top: 5px;
|
||||
}
|
||||
.badge {
|
||||
display: inline-block;
|
||||
padding: 6px 14px;
|
||||
border-radius: 15px;
|
||||
font-weight: 600;
|
||||
font-size: 0.9rem;
|
||||
}
|
||||
.badge-success { background: #d4edda; color: #155724; }
|
||||
.badge-danger { background: #f8d7da; color: #721c24; }
|
||||
.severity-card { border-left-color: #e74c3c; }
|
||||
.severity-badge {
|
||||
padding: 8px 16px;
|
||||
border-radius: 20px;
|
||||
color: white;
|
||||
font-weight: 700;
|
||||
text-transform: uppercase;
|
||||
font-size: 0.9rem;
|
||||
}
|
||||
.severity-details {
|
||||
margin-top: 10px;
|
||||
font-size: 0.9rem;
|
||||
color: #555;
|
||||
line-height: 1.5;
|
||||
}
|
||||
.severity-stats {
|
||||
display: grid;
|
||||
grid-template-columns: 1fr 1fr;
|
||||
gap: 10px;
|
||||
margin-top: 10px;
|
||||
}
|
||||
.stat-box {
|
||||
background: white;
|
||||
padding: 10px;
|
||||
border-radius: 8px;
|
||||
text-align: center;
|
||||
}
|
||||
.stat-label {
|
||||
font-size: 0.75rem;
|
||||
color: #7f8c8d;
|
||||
margin-bottom: 4px;
|
||||
}
|
||||
.stat-value {
|
||||
font-size: 0.95rem;
|
||||
font-weight: 700;
|
||||
color: #2c3e50;
|
||||
}
|
||||
.loading {
|
||||
text-align: center;
|
||||
padding: 40px;
|
||||
display: none;
|
||||
}
|
||||
.spinner {
|
||||
border: 4px solid #f3f3f3;
|
||||
border-top: 4px solid #3498db;
|
||||
border-radius: 50%;
|
||||
width: 40px;
|
||||
height: 40px;
|
||||
animation: spin 1s linear infinite;
|
||||
margin: 0 auto 20px;
|
||||
}
|
||||
@keyframes spin {
|
||||
0% { transform: rotate(0deg); }
|
||||
100% { transform: rotate(360deg); }
|
||||
}
|
||||
.error {
|
||||
background: #f8d7da;
|
||||
color: #721c24;
|
||||
padding: 15px;
|
||||
border-radius: 10px;
|
||||
margin: 15px 0;
|
||||
display: none;
|
||||
}
|
||||
#fileInput { display: none; }
|
||||
.no-results-message {
|
||||
text-align: center;
|
||||
color: #7f8c8d;
|
||||
padding: 60px 20px;
|
||||
font-size: 1rem;
|
||||
}
|
||||
.info-badge {
|
||||
background: #e3f2fd;
|
||||
color: #1976d2;
|
||||
padding: 8px 12px;
|
||||
border-radius: 8px;
|
||||
font-size: 0.8rem;
|
||||
margin-top: 8px;
|
||||
display: block;
|
||||
line-height: 1.4;
|
||||
}
|
||||
.measurement-main {
|
||||
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
|
||||
color: white;
|
||||
padding: 20px;
|
||||
border-radius: 12px;
|
||||
text-align: center;
|
||||
margin-bottom: 12px;
|
||||
}
|
||||
.measurement-value {
|
||||
font-size: 2.5rem;
|
||||
font-weight: 900;
|
||||
margin: 10px 0;
|
||||
text-shadow: 2px 2px 4px rgba(0,0,0,0.3);
|
||||
}
|
||||
.measurement-label {
|
||||
font-size: 0.9rem;
|
||||
opacity: 0.9;
|
||||
}
|
||||
.measurement-details {
|
||||
display: grid;
|
||||
grid-template-columns: 1fr 1fr;
|
||||
gap: 10px;
|
||||
}
|
||||
.detail-box {
|
||||
background: white;
|
||||
padding: 12px;
|
||||
border-radius: 8px;
|
||||
text-align: center;
|
||||
}
|
||||
.detail-label {
|
||||
font-size: 0.75rem;
|
||||
color: #7f8c8d;
|
||||
margin-bottom: 4px;
|
||||
}
|
||||
.detail-value {
|
||||
font-size: 1.1rem;
|
||||
font-weight: 700;
|
||||
color: #2c3e50;
|
||||
}
|
||||
|
||||
/* Modal Styles - Expanded */
|
||||
.modal-overlay {
|
||||
position: fixed;
|
||||
top: 0;
|
||||
left: 0;
|
||||
width: 100%;
|
||||
height: 100%;
|
||||
background: rgba(0, 0, 0, 0.7);
|
||||
backdrop-filter: blur(12px);
|
||||
display: none;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
z-index: 1000;
|
||||
opacity: 0;
|
||||
transition: opacity 0.3s ease;
|
||||
padding: 20px;
|
||||
}
|
||||
|
||||
.modal-overlay.active {
|
||||
display: flex;
|
||||
opacity: 1;
|
||||
}
|
||||
|
||||
.modal-content {
|
||||
background: white;
|
||||
width: 100%;
|
||||
max-width: 1000px;
|
||||
max-height: 95vh;
|
||||
border-radius: 24px;
|
||||
box-shadow: 0 25px 50px -12px rgba(0, 0, 0, 0.3);
|
||||
overflow-y: auto;
|
||||
transform: scale(0.9) translateY(30px);
|
||||
transition: all 0.5s cubic-bezier(0.34, 1.56, 0.64, 1);
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
}
|
||||
|
||||
.modal-overlay.active .modal-content {
|
||||
transform: scale(1) translateY(0);
|
||||
}
|
||||
|
||||
.modal-header {
|
||||
background: linear-gradient(135deg, #2c3e50 0%, #3498db 100%);
|
||||
color: white;
|
||||
padding: 20px 30px;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: space-between;
|
||||
position: sticky;
|
||||
top: 0;
|
||||
z-index: 10;
|
||||
}
|
||||
|
||||
.modal-header h2 {
|
||||
font-size: 1.4rem;
|
||||
font-weight: 800;
|
||||
}
|
||||
|
||||
.close-modal {
|
||||
background: rgba(255, 255, 255, 0.2);
|
||||
border: none;
|
||||
color: white;
|
||||
font-size: 1.5rem;
|
||||
width: 40px;
|
||||
height: 40px;
|
||||
border-radius: 50%;
|
||||
cursor: pointer;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
transition: all 0.2s;
|
||||
}
|
||||
|
||||
.close-modal:hover {
|
||||
background: rgba(255, 255, 255, 0.3);
|
||||
transform: rotate(90deg);
|
||||
}
|
||||
|
||||
.modal-body {
|
||||
padding: 30px;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 25px;
|
||||
}
|
||||
|
||||
/* Image Grid in Modal */
|
||||
.modal-images-grid {
|
||||
display: grid;
|
||||
grid-template-columns: 1fr 1fr;
|
||||
gap: 20px;
|
||||
}
|
||||
|
||||
.modal-img-box {
|
||||
background: #f1f3f5;
|
||||
padding: 10px;
|
||||
border-radius: 12px;
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
.img-label {
|
||||
display: block;
|
||||
font-size: 0.85rem;
|
||||
font-weight: 700;
|
||||
color: #495057;
|
||||
margin-bottom: 8px;
|
||||
text-transform: uppercase;
|
||||
}
|
||||
|
||||
.modal-img-box img {
|
||||
width: 100%;
|
||||
border-radius: 8px;
|
||||
box-shadow: 0 4px 10px rgba(0,0,0,0.1);
|
||||
}
|
||||
|
||||
/* Details Grid in Modal */
|
||||
.modal-details-grid {
|
||||
display: grid;
|
||||
grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
|
||||
gap: 15px;
|
||||
}
|
||||
|
||||
.modal-detail-card {
|
||||
background: #f8f9fa;
|
||||
padding: 15px;
|
||||
border-radius: 16px;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
text-align: center;
|
||||
border: 1px solid #e9ecef;
|
||||
}
|
||||
|
||||
.modal-detail-card.highlight {
|
||||
background: linear-gradient(135deg, #f0f7ff 0%, #e3f2fd 100%);
|
||||
border-color: #bbdefb;
|
||||
}
|
||||
|
||||
.modal-detail-card .label {
|
||||
font-size: 0.8rem;
|
||||
color: #6c757d;
|
||||
font-weight: 600;
|
||||
margin-bottom: 5px;
|
||||
}
|
||||
|
||||
.modal-detail-card .value {
|
||||
font-size: 1.25rem;
|
||||
font-weight: 800;
|
||||
color: #2c3e50;
|
||||
}
|
||||
|
||||
.modal-legend {
|
||||
background: white;
|
||||
padding: 20px;
|
||||
border-radius: 16px;
|
||||
border: 1px solid #dee2e6;
|
||||
}
|
||||
|
||||
.modal-legend h4 {
|
||||
margin-bottom: 12px;
|
||||
font-size: 0.95rem;
|
||||
}
|
||||
|
||||
.modal-advice {
|
||||
background: #fff9db;
|
||||
padding: 20px;
|
||||
border-radius: 16px;
|
||||
border-left: 5px solid #fcc419;
|
||||
color: #856404;
|
||||
font-size: 1rem;
|
||||
line-height: 1.6;
|
||||
}
|
||||
|
||||
.modal-footer {
|
||||
padding: 0 30px 40px;
|
||||
display: flex;
|
||||
justify-content: center;
|
||||
}
|
||||
|
||||
.modal-btn-primary {
|
||||
background: linear-gradient(135deg, #228be6, #1c7ed6);
|
||||
color: white;
|
||||
border: none;
|
||||
padding: 16px 60px;
|
||||
border-radius: 35px;
|
||||
font-size: 1.2rem;
|
||||
font-weight: 800;
|
||||
cursor: pointer;
|
||||
box-shadow: 0 10px 25px -5px rgba(34, 139, 230, 0.4);
|
||||
transition: all 0.3s;
|
||||
}
|
||||
|
||||
.modal-btn-primary:hover {
|
||||
transform: translateY(-4px);
|
||||
box-shadow: 0 15px 30px -5px rgba(34, 139, 230, 0.5);
|
||||
}
|
||||
|
||||
/* Responsive Modal */
|
||||
@media (max-width: 768px) {
|
||||
.modal-images-grid {
|
||||
grid-template-columns: 1fr;
|
||||
}
|
||||
.modal-content {
|
||||
max-height: 100vh;
|
||||
border-radius: 0;
|
||||
}
|
||||
.modal-overlay {
|
||||
padding: 0;
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,288 @@
|
||||
<!DOCTYPE html>
|
||||
<html lang="vi">
|
||||
<head>
|
||||
<meta charset="UTF-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
<title>Phân tích ảnh siêu âm khớp gối</title>
|
||||
|
||||
<!-- Link CSS -->
|
||||
<link rel="stylesheet" href="css/style.css">
|
||||
</head>
|
||||
<body>
|
||||
<div class="container">
|
||||
<div class="header">
|
||||
<h1>🏥 PHÂN TÍCH ẢNH SIÊU ÂM KHỚP GỐI</h1>
|
||||
<p>Hệ thống phân tích tự động với AI - Pipeline: Góc → Viêm → Phân đoạn → Đo đạc</p>
|
||||
</div>
|
||||
|
||||
<div class="main-content">
|
||||
<div class="models-panel">
|
||||
<h3>⚙️ Chọn mô hình</h3>
|
||||
|
||||
<div class="model-section">
|
||||
<h4>1️⃣ Phân loại góc chụp</h4>
|
||||
<select id="angleModel">
|
||||
<option value="convnext">ConvNeXt-Tiny (100%)</option>
|
||||
<option value="densenet">DenseNet121 (98%)</option>
|
||||
<option value="resnet50">ResNet50 (98%)</option>
|
||||
<option value="efficientnet_b2">EfficientNet-B2 (97.78%)</option>
|
||||
<option value="swin">Swin Transformer (97.78%)</option>
|
||||
</select>
|
||||
<small>Luôn chạy đầu tiên cho mọi ảnh</small>
|
||||
</div>
|
||||
|
||||
<div class="model-section">
|
||||
<h4>2️⃣ Phát hiện viêm</h4>
|
||||
<select id="inflammationModel">
|
||||
<option value="efficientnet_b0">EfficientNet-B0</option>
|
||||
</select>
|
||||
<small>Chỉ với góc post-trans và suprapat-long</small>
|
||||
</div>
|
||||
|
||||
<div class="model-section">
|
||||
<h4>3️⃣ Phân đoạn Suprapat</h4>
|
||||
<select id="segmentModelSup">
|
||||
<option value="deeplabv3">DeepLabV3-ResNet50 (91.67%)</option>
|
||||
<option value="efficientfeedback">EfficientFeedback (86%)</option>
|
||||
<option value="unet3plus">UNet3+ Attention (80%)</option>
|
||||
<option value="unet_resnet101">UNet-ResNet101 (73.62%)</option>
|
||||
</select>
|
||||
<small>Dùng cho góc suprapat-long</small>
|
||||
</div>
|
||||
|
||||
<div class="model-section">
|
||||
<h4>4️⃣ Phân đoạn Post-trans</h4>
|
||||
<select id="segmentModelPost">
|
||||
<option value="deeplabv3_resnet101">DeepLabV3-ResNet101</option>
|
||||
</select>
|
||||
<small>Dùng cho góc post-trans</small>
|
||||
</div>
|
||||
|
||||
<div class="info-badge">
|
||||
💡 Đo độ dày (chỉ SUP): Quét vùng 1/3 giữa, tìm đoạn liên tục dài nhất
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="upload-panel">
|
||||
<div id="uploadSection">
|
||||
<h2 class="panel-title">📤 Tải ảnh lên</h2>
|
||||
|
||||
<div class="upload-area" id="uploadArea">
|
||||
<div class="upload-icon">📷</div>
|
||||
<div style="margin-bottom: 15px; color: #7f8c8d;">
|
||||
Kéo thả ảnh vào đây hoặc
|
||||
</div>
|
||||
<button class="upload-btn" onclick="document.getElementById('fileInput').click()">
|
||||
Chọn ảnh
|
||||
</button>
|
||||
<input type="file" id="fileInput" accept="image/*">
|
||||
</div>
|
||||
|
||||
<div class="loading" id="loading">
|
||||
<div class="spinner"></div>
|
||||
<p>Đang phân tích ảnh...</p>
|
||||
</div>
|
||||
|
||||
<div class="error" id="error"></div>
|
||||
</div>
|
||||
|
||||
<div id="originalImageContainer" style="display: none;">
|
||||
<h2 class="panel-title">📷 Ảnh gốc</h2>
|
||||
<div class="image-preview-box">
|
||||
<img id="originalImage" class="preview-image">
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="results-panel">
|
||||
<h2 class="panel-title">📊 Kết quả phân tích</h2>
|
||||
|
||||
<div id="resultsContent">
|
||||
<!-- Patient Information Form -->
|
||||
<div class="patient-panel" id="patientInfoPanel" style="display: none;">
|
||||
<h3 class="panel-title" style="font-size: 1.1rem;">👤 Thông tin bệnh nhân</h3>
|
||||
<div class="form-grid">
|
||||
<div class="form-group">
|
||||
<label for="patientName">Họ và tên <span>*</span></label>
|
||||
<input type="text" id="patientName" placeholder="Nhập tên bệnh nhân...">
|
||||
</div>
|
||||
<div class="form-group">
|
||||
<label for="patientId">Mã bệnh nhân <span>*</span></label>
|
||||
<input type="text" id="patientId" placeholder="VD: BN001...">
|
||||
</div>
|
||||
<div class="form-group">
|
||||
<label for="patientGender">Giới tính</label>
|
||||
<select id="patientGender">
|
||||
<option value="Nam">Nam</option>
|
||||
<option value="Nữ">Nữ</option>
|
||||
<option value="Khác">Khác</option>
|
||||
</select>
|
||||
</div>
|
||||
<div class="form-group">
|
||||
<label for="patientAge">Tuổi</label>
|
||||
<input type="number" id="patientAge" placeholder="Nhập tuổi...">
|
||||
</div>
|
||||
<div class="form-group full-width">
|
||||
<label for="doctorDiagnosis">Chẩn đoán của bác sĩ</label>
|
||||
<textarea id="doctorDiagnosis" rows="3" placeholder="Nhập chẩn đoán hoặc ghi chú thêm..."></textarea>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="save-section">
|
||||
<button id="saveDataBtn" class="save-btn" disabled>
|
||||
<span class="icon">💾</span> Lưu dữ liệu
|
||||
</button>
|
||||
<button id="exportPdfBtn" class="export-btn" disabled>
|
||||
<span class="icon">📄</span> Xuất phiếu khám (PDF)
|
||||
</button>
|
||||
<div id="saveStatus" style="margin-top: 10px; font-size: 0.85rem;"></div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div id="angleResultCard" style="display: none;">
|
||||
<div class="angle-result-card">
|
||||
<h3>🎯 GÓC CHỤP</h3>
|
||||
<div class="angle-value" id="angleValue">-</div>
|
||||
<div class="angle-confidence" id="angleConfidenceText">-</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div id="segmentedImageContainer" style="display: none;">
|
||||
<div class="result-image-box">
|
||||
<h3>🎨 Ảnh phân đoạn</h3>
|
||||
<img id="segmentedImage">
|
||||
|
||||
<div class="color-legend" id="colorLegend" style="display: none;">
|
||||
<h4>📋 Chú thích màu sắc:</h4>
|
||||
<div class="legend-items" id="legendItems"></div>
|
||||
<div style="margin-top: 8px; padding-top: 8px; border-top: 1px solid #e0e0e0; font-size: 0.8rem; color: #666;">
|
||||
⚠️ <strong>Viền trắng dày</strong>: Vùng viêm (dịch khớp & màng hoạt dịch)<br>
|
||||
<span id="measurementNote" style="display: none;">📏 <strong>Đường đỏ</strong>: Đo độ dày tại vùng 1/3 giữa</span>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div id="resultsGrid" style="display: none;">
|
||||
<div class="results-grid">
|
||||
<div class="result-card full-width" id="inflammationCard" style="display: none;">
|
||||
<h4>🔬 Tình trạng viêm</h4>
|
||||
<div id="inflammationResult"></div>
|
||||
<div class="confidence" id="inflammationConfidence">-</div>
|
||||
</div>
|
||||
|
||||
<div class="result-card full-width" id="measurementCard" style="display: none; border-left-color: #2ecc71; padding: 0; overflow: hidden;">
|
||||
<div style="padding: 15px;">
|
||||
<h4>📏 Đo độ dày (Dịch + Màng hoạt dịch)</h4>
|
||||
</div>
|
||||
|
||||
<div class="measurement-main">
|
||||
<div class="measurement-label">Độ dày tối đa</div>
|
||||
<div class="measurement-value" id="thicknessMm">- mm</div>
|
||||
<div class="measurement-label">
|
||||
(<span id="thicknessPx">-</span> pixels)
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div style="padding: 0 15px 15px 15px;">
|
||||
<div class="measurement-details">
|
||||
<div class="detail-box">
|
||||
<div class="detail-label">Vị trí X</div>
|
||||
<div class="detail-value" id="measurementLocationX">-</div>
|
||||
</div>
|
||||
<div class="detail-box">
|
||||
<div class="detail-label">Phạm vi Y</div>
|
||||
<div class="detail-value" id="measurementRangeY">-</div>
|
||||
</div>
|
||||
</div>
|
||||
<div style="margin-top: 8px; font-size: 0.75rem; color: #7f8c8d; text-align: center;">
|
||||
ℹ️ Đo tại vùng 1/3 giữa bounding box - Tìm đoạn liên tục dài nhất
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="result-card severity-card full-width" id="severityCard" style="display: none;">
|
||||
<h4>📈 Mức độ viêm</h4>
|
||||
<div>
|
||||
<span class="severity-badge" id="severityBadge">-</span>
|
||||
</div>
|
||||
<div class="severity-details" id="severityDescription">-</div>
|
||||
<div class="severity-stats">
|
||||
<div class="stat-box">
|
||||
<div class="stat-label">Dịch khớp (Effusion)</div>
|
||||
<div class="stat-value" id="effusionStat">-</div>
|
||||
</div>
|
||||
<div class="stat-box">
|
||||
<div class="stat-label">Màng hoạt dịch (Synovium)</div>
|
||||
<div class="stat-value" id="synoviumStat">-</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div id="noResults" class="no-results-message">
|
||||
Chưa có kết quả phân tích
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Popup Kết quả chẩn đoán -->
|
||||
<div id="resultModal" class="modal-overlay">
|
||||
<div class="modal-content">
|
||||
<div class="modal-header">
|
||||
<h2>🩺 KẾT QUẢ CHẨN ĐOÁN AI</h2>
|
||||
<button class="close-modal" id="closeModal">×</button>
|
||||
</div>
|
||||
<div class="modal-body">
|
||||
<div class="modal-images-grid">
|
||||
<div class="modal-img-box">
|
||||
<span class="img-label">Ảnh tăng cường</span>
|
||||
<img id="modalImgEnhanced" src="" alt="Enhanced">
|
||||
</div>
|
||||
<div class="modal-img-box" id="modalImgSegmentedBox">
|
||||
<span class="img-label">Ảnh phân đoạn</span>
|
||||
<img id="modalImgSegmented" src="" alt="Segmented">
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="modal-details-grid">
|
||||
<div class="modal-detail-card">
|
||||
<span class="label">📐 Góc chụp</span>
|
||||
<span class="value" id="modalAngle">-</span>
|
||||
</div>
|
||||
<div class="modal-detail-card" id="modalInflammationRow">
|
||||
<span class="label">🔥 Tình trạng</span>
|
||||
<span class="value" id="modalInflammation">-</span>
|
||||
</div>
|
||||
<div class="modal-detail-card highlight" id="modalMeasurementRow" style="display: none;">
|
||||
<span class="label">📏 Độ dày</span>
|
||||
<span class="value" id="modalThickness">- mm</span>
|
||||
</div>
|
||||
<div class="modal-detail-card highlight" id="modalSeverityRow" style="display: none;">
|
||||
<span class="label">📈 Mức độ</span>
|
||||
<span class="severity-badge" id="modalSeverity">-</span>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div id="modalLegendContainer" class="modal-legend" style="display: none;">
|
||||
<h4>📋 Chú thích màu sắc:</h4>
|
||||
<div id="modalLegendItems" class="legend-items"></div>
|
||||
</div>
|
||||
|
||||
<div class="modal-advice">
|
||||
<p id="modalAdviceText">Hệ thống AI đã hoàn tất phân tích. Vui lòng đối chiếu hình ảnh và kết quả đo đạc phía trên.</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="modal-footer">
|
||||
<button class="modal-btn-primary" id="modalViewDetail">Xem chi tiết</button>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Link JavaScript -->
|
||||
<script src="js/script.js?v=1.1"></script>
|
||||
</body>
|
||||
</html>
|
||||
@@ -0,0 +1,440 @@
|
||||
const API_BASE = 'http://127.0.0.1:8000';
|
||||
const uploadArea = document.getElementById('uploadArea');
|
||||
const fileInput = document.getElementById('fileInput');
|
||||
let currentResult = null;
|
||||
let originalImageBase64 = null;
|
||||
|
||||
const ANGLE_NAMES = {
|
||||
'med-lat': 'Med-Lat Long',
|
||||
'post-trans': 'Post Trans',
|
||||
'sup-trans-flex': 'Suprapat Trans Flex',
|
||||
'sup-up-long': 'Suprapat Up Long'
|
||||
};
|
||||
|
||||
uploadArea.addEventListener('dragover', (e) => {
|
||||
e.preventDefault();
|
||||
uploadArea.style.borderColor = '#3498db';
|
||||
uploadArea.style.background = '#f8f9fa';
|
||||
});
|
||||
|
||||
uploadArea.addEventListener('dragleave', () => {
|
||||
uploadArea.style.borderColor = '#bdc3c7';
|
||||
uploadArea.style.background = 'transparent';
|
||||
});
|
||||
|
||||
uploadArea.addEventListener('drop', (e) => {
|
||||
e.preventDefault();
|
||||
uploadArea.style.borderColor = '#bdc3c7';
|
||||
uploadArea.style.background = 'transparent';
|
||||
const files = e.dataTransfer.files;
|
||||
if (files.length > 0) handleFile(files[0]);
|
||||
});
|
||||
|
||||
fileInput.addEventListener('change', (e) => {
|
||||
if (e.target.files.length > 0) handleFile(e.target.files[0]);
|
||||
});
|
||||
|
||||
function handleFile(file) {
|
||||
if (!file.type.startsWith('image/')) {
|
||||
showError('Vui lòng chọn file ảnh hợp lệ');
|
||||
return;
|
||||
}
|
||||
|
||||
const reader = new FileReader();
|
||||
reader.onload = (e) => {
|
||||
originalImageBase64 = e.target.result;
|
||||
document.getElementById('originalImage').src = originalImageBase64;
|
||||
document.getElementById('originalImageContainer').style.display = 'block';
|
||||
};
|
||||
reader.readAsDataURL(file);
|
||||
|
||||
uploadAndAnalyze(file);
|
||||
}
|
||||
|
||||
async function uploadAndAnalyze(file) {
|
||||
showLoading(true);
|
||||
hideError();
|
||||
hideResults();
|
||||
|
||||
const angleModelValue = document.getElementById('angleModel').value;
|
||||
const inflammationModelValue = document.getElementById('inflammationModel').value;
|
||||
const segmentModelSupValue = document.getElementById('segmentModelSup').value;
|
||||
const segmentModelPostValue = document.getElementById('segmentModelPost').value;
|
||||
|
||||
console.log('🎯 Selected models:', {
|
||||
angle: angleModelValue,
|
||||
inflammation: inflammationModelValue,
|
||||
seg_sup: segmentModelSupValue,
|
||||
seg_post: segmentModelPostValue
|
||||
});
|
||||
|
||||
const formData = new FormData();
|
||||
formData.append('image', file);
|
||||
|
||||
try {
|
||||
const url = `${API_BASE}/api/analyze?angle_model=${angleModelValue}&inflammation_model=${inflammationModelValue}&segment_model_sup=${segmentModelSupValue}&segment_model_post=${segmentModelPostValue}`;
|
||||
console.log('🌐 Request URL:', url);
|
||||
|
||||
const response = await fetch(url, {
|
||||
method: 'POST',
|
||||
body: formData
|
||||
});
|
||||
|
||||
if (!response.ok) throw new Error('Lỗi phân tích');
|
||||
|
||||
const result = await response.json();
|
||||
console.log('✅ TRẢ VỀ TỪ API:', result);
|
||||
|
||||
if (result.success) {
|
||||
currentResult = result;
|
||||
displayResults(result);
|
||||
}
|
||||
else showError('Phân tích thất bại');
|
||||
|
||||
} catch (error) {
|
||||
console.error('❌ Error:', error);
|
||||
showError(`Lỗi: ${error.message}`);
|
||||
} finally {
|
||||
showLoading(false);
|
||||
}
|
||||
}
|
||||
|
||||
function displayResults(result) {
|
||||
try {
|
||||
console.log('--- ĐANG HIỂN THỊ KẾT QUẢ ---');
|
||||
document.getElementById('noResults').style.display = 'none';
|
||||
|
||||
// Cập nhật ảnh gốc thành ảnh đã tăng cường tương phản (CLAHE)
|
||||
if (result.images && result.images.enhanced) {
|
||||
document.getElementById('originalImage').src = result.images.enhanced;
|
||||
}
|
||||
|
||||
document.getElementById('angleResultCard').style.display = 'block';
|
||||
document.getElementById('angleValue').textContent = ANGLE_NAMES[result.angle.class] || result.angle.class;
|
||||
document.getElementById('angleConfidenceText').textContent = `Độ tin cậy: ${result.angle.confidence}%`;
|
||||
|
||||
if (result.inflammation) {
|
||||
console.log('✅ Hiển thị Inflammation');
|
||||
document.getElementById('resultsGrid').style.display = 'block';
|
||||
document.getElementById('inflammationCard').style.display = 'block';
|
||||
|
||||
const inflDiv = document.getElementById('inflammationResult');
|
||||
if (result.inflammation.detected) {
|
||||
inflDiv.innerHTML = '<span class="badge badge-danger">CÓ KHẢ NĂNG VIÊM / THEO DÕI VIÊM</span>';
|
||||
} else {
|
||||
inflDiv.innerHTML = '<span class="badge badge-success">KHÔNG VIÊM</span>';
|
||||
}
|
||||
document.getElementById('inflammationConfidence').textContent = `Độ tin cậy: ${result.inflammation.confidence}%`;
|
||||
}
|
||||
|
||||
if (result.segmentation && result.segmentation.performed) {
|
||||
console.log('✅ Hiển thị Segmentation & Overlay');
|
||||
document.getElementById('segmentedImageContainer').style.display = 'block';
|
||||
document.getElementById('segmentedImage').src = result.images.segmented;
|
||||
|
||||
if (result.segmentation.color_legend) {
|
||||
displayColorLegend(result.segmentation.color_legend, result.segmentation.angle_type);
|
||||
}
|
||||
|
||||
if (result.segmentation.angle_type === 'sup') {
|
||||
document.getElementById('measurementNote').style.display = 'inline';
|
||||
} else {
|
||||
document.getElementById('measurementNote').style.display = 'none';
|
||||
}
|
||||
}
|
||||
|
||||
if (result.measurement) {
|
||||
console.log('✅ Hiển thị Đo đạc (Measurement)');
|
||||
document.getElementById('measurementCard').style.display = 'block';
|
||||
|
||||
document.getElementById('thicknessMm').textContent = `${result.measurement.thickness_mm} mm`;
|
||||
document.getElementById('thicknessPx').textContent = `${result.measurement.thickness_px}`;
|
||||
document.getElementById('measurementLocationX').textContent = result.measurement.location_x;
|
||||
document.getElementById('measurementRangeY').textContent =
|
||||
`${result.measurement.y_start} - ${result.measurement.y_end}`;
|
||||
}
|
||||
|
||||
if (result.severity) {
|
||||
console.log('✅ Hiển thị Mức độ (Severity)');
|
||||
document.getElementById('severityCard').style.display = 'block';
|
||||
const badge = document.getElementById('severityBadge');
|
||||
badge.textContent = result.severity.severity;
|
||||
badge.style.background = result.severity.color;
|
||||
document.getElementById('severityDescription').textContent = result.severity.description;
|
||||
document.getElementById('effusionStat').textContent =
|
||||
`${result.severity.effusion.ratio}% (${result.severity.effusion.thickness}px)`;
|
||||
document.getElementById('synoviumStat').textContent = `${result.severity.synovium.ratio}%`;
|
||||
}
|
||||
|
||||
// Hiện bảng nhập liệu bệnh nhân
|
||||
document.getElementById('patientInfoPanel').style.display = 'block';
|
||||
updateSaveButtonState();
|
||||
|
||||
// HIỂN THỊ POPUP KẾT QUẢ
|
||||
showResultPopup(result);
|
||||
|
||||
} catch (err) {
|
||||
console.error('❌ LỖI TRONG displayResults:', err);
|
||||
showError(`Lỗi hiển thị: ${err.message}`);
|
||||
}
|
||||
}
|
||||
|
||||
// Kiểm tra tính hợp lệ của form
|
||||
function updateSaveButtonState() {
|
||||
const name = document.getElementById('patientName').value.trim();
|
||||
const id = document.getElementById('patientId').value.trim();
|
||||
const btn = document.getElementById('saveDataBtn');
|
||||
const exportBtn = document.getElementById('exportPdfBtn');
|
||||
|
||||
const isValid = !!(currentResult && name && id);
|
||||
btn.disabled = !isValid;
|
||||
exportBtn.disabled = !isValid;
|
||||
}
|
||||
|
||||
// Lắng nghe thay đổi trên form
|
||||
['patientName', 'patientId', 'patientGender', 'patientAge', 'doctorDiagnosis'].forEach(id => {
|
||||
document.getElementById(id).addEventListener('input', updateSaveButtonState);
|
||||
});
|
||||
|
||||
// Hàm lưu dữ liệu
|
||||
document.getElementById('saveDataBtn').addEventListener('click', async () => {
|
||||
if (!currentResult) return;
|
||||
|
||||
const saveBtn = document.getElementById('saveDataBtn');
|
||||
const statusDiv = document.getElementById('saveStatus');
|
||||
|
||||
const payload = {
|
||||
patient_info: {
|
||||
name: document.getElementById('patientName').value,
|
||||
id: document.getElementById('patientId').value,
|
||||
gender: document.getElementById('patientGender').value,
|
||||
age: document.getElementById('patientAge').value,
|
||||
diagnosis: document.getElementById('doctorDiagnosis').value
|
||||
},
|
||||
analysis_result: currentResult,
|
||||
images: {
|
||||
original: originalImageBase64,
|
||||
segmented: currentResult.images.segmented
|
||||
}
|
||||
};
|
||||
|
||||
try {
|
||||
saveBtn.disabled = true;
|
||||
statusDiv.innerHTML = '<span style="color: blue;">⌛ Đang lưu...</span>';
|
||||
|
||||
const response = await fetch(`${API_BASE}/api/save`, {
|
||||
method: 'POST',
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
body: JSON.stringify(payload)
|
||||
});
|
||||
|
||||
const resData = await response.json();
|
||||
if (resData.success) {
|
||||
statusDiv.innerHTML = `<span style="color: green;">✅ Đã lưu vào thư mục: <strong>${resData.folder}</strong></span>`;
|
||||
} else {
|
||||
throw new Error(resData.detail || 'Lỗi không xác định');
|
||||
}
|
||||
} catch (error) {
|
||||
console.error('❌ Save error:', error);
|
||||
statusDiv.innerHTML = `<span style="color: red;">❌ Lỗi: ${error.message}</span>`;
|
||||
} finally {
|
||||
saveBtn.disabled = false;
|
||||
}
|
||||
});
|
||||
|
||||
document.getElementById('exportPdfBtn').addEventListener('click', async () => {
|
||||
if (!currentResult) return;
|
||||
|
||||
const exportBtn = document.getElementById('exportPdfBtn');
|
||||
const statusDiv = document.getElementById('saveStatus');
|
||||
|
||||
const payload = {
|
||||
patient_info: {
|
||||
name: document.getElementById('patientName').value,
|
||||
id: document.getElementById('patientId').value,
|
||||
gender: document.getElementById('patientGender').value,
|
||||
age: document.getElementById('patientAge').value,
|
||||
diagnosis: document.getElementById('doctorDiagnosis').value
|
||||
},
|
||||
analysis_result: currentResult,
|
||||
images: {
|
||||
original: originalImageBase64,
|
||||
segmented: currentResult.images.segmented
|
||||
}
|
||||
};
|
||||
|
||||
try {
|
||||
exportBtn.disabled = true;
|
||||
statusDiv.innerHTML = '<span style="color: blue;">⌛ Đang khởi tạo PDF...</span>';
|
||||
|
||||
const response = await fetch(`${API_BASE}/api/export-pdf`, {
|
||||
method: 'POST',
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
body: JSON.stringify(payload)
|
||||
});
|
||||
|
||||
if (!response.ok) throw new Error('Lỗi từ server khi tạo PDF');
|
||||
|
||||
const blob = await response.blob();
|
||||
const url = window.URL.createObjectURL(blob);
|
||||
const a = document.createElement('a');
|
||||
a.href = url;
|
||||
a.download = `Phieu_Kham_${payload.patient_info.id || 'BN'}.pdf`;
|
||||
document.body.appendChild(a);
|
||||
a.click();
|
||||
window.URL.revokeObjectURL(url);
|
||||
document.body.removeChild(a);
|
||||
|
||||
statusDiv.innerHTML = `<span style="color: green;">✅ Đã xuất file PDF!</span>`;
|
||||
} catch (error) {
|
||||
console.error('❌ Export error:', error);
|
||||
statusDiv.innerHTML = `<span style="color: red;">❌ Lỗi: ${error.message}</span>`;
|
||||
} finally {
|
||||
exportBtn.disabled = false;
|
||||
}
|
||||
});
|
||||
|
||||
function displayColorLegend(colorLegend, angleType) {
|
||||
const legendContainer = document.getElementById('legendItems');
|
||||
legendContainer.innerHTML = '';
|
||||
|
||||
colorLegend.forEach(item => {
|
||||
const isHighlight = item.key === 'effusion' || item.key === 'synovium';
|
||||
const legendItem = document.createElement('div');
|
||||
legendItem.className = 'legend-item';
|
||||
legendItem.innerHTML = `
|
||||
<div class="legend-color ${isHighlight ? 'legend-highlight' : ''}"
|
||||
style="background-color: rgb(${item.color.join(',')})"></div>
|
||||
<span>${item.name}</span>
|
||||
`;
|
||||
legendContainer.appendChild(legendItem);
|
||||
});
|
||||
|
||||
document.getElementById('colorLegend').style.display = 'block';
|
||||
}
|
||||
|
||||
function showLoading(show) {
|
||||
document.getElementById('loading').style.display = show ? 'block' : 'none';
|
||||
}
|
||||
|
||||
function showError(msg) {
|
||||
const errorDiv = document.getElementById('error');
|
||||
errorDiv.textContent = msg;
|
||||
errorDiv.style.display = 'block';
|
||||
}
|
||||
|
||||
function hideError() {
|
||||
document.getElementById('error').style.display = 'none';
|
||||
}
|
||||
|
||||
function hideResults() {
|
||||
document.getElementById('angleResultCard').style.display = 'none';
|
||||
document.getElementById('resultsGrid').style.display = 'none';
|
||||
document.getElementById('segmentedImageContainer').style.display = 'none';
|
||||
document.getElementById('inflammationCard').style.display = 'none';
|
||||
document.getElementById('measurementCard').style.display = 'none';
|
||||
document.getElementById('severityCard').style.display = 'none';
|
||||
document.getElementById('noResults').style.display = 'block';
|
||||
}
|
||||
|
||||
function showResultPopup(result) {
|
||||
const modal = document.getElementById('resultModal');
|
||||
|
||||
// 1. Hình ảnh
|
||||
if (result.images) {
|
||||
document.getElementById('modalImgEnhanced').src = result.images.enhanced || '';
|
||||
const segImg = document.getElementById('modalImgSegmented');
|
||||
const segBox = document.getElementById('modalImgSegmentedBox');
|
||||
|
||||
if (result.images.segmented) {
|
||||
segImg.src = result.images.segmented;
|
||||
segBox.style.display = 'block';
|
||||
} else {
|
||||
segBox.style.display = 'none';
|
||||
}
|
||||
}
|
||||
|
||||
// 2. Chi tiết kết quả
|
||||
document.getElementById('modalAngle').textContent = ANGLE_NAMES[result.angle.class] || result.angle.class;
|
||||
|
||||
const inflEl = document.getElementById('modalInflammation');
|
||||
if (result.inflammation) {
|
||||
document.getElementById('modalInflammationRow').style.display = 'flex';
|
||||
if (result.inflammation.detected) {
|
||||
inflEl.innerHTML = '<span class="badge badge-danger">CÓ VIÊM/THEO DÕI</span>';
|
||||
} else {
|
||||
inflEl.innerHTML = '<span class="badge badge-success">KHÔNG VIÊM</span>';
|
||||
}
|
||||
} else {
|
||||
document.getElementById('modalInflammationRow').style.display = 'none';
|
||||
}
|
||||
|
||||
if (result.measurement) {
|
||||
document.getElementById('modalMeasurementRow').style.display = 'flex';
|
||||
document.getElementById('modalThickness').textContent = `${result.measurement.thickness_mm} mm`;
|
||||
} else {
|
||||
document.getElementById('modalMeasurementRow').style.display = 'none';
|
||||
}
|
||||
|
||||
if (result.severity) {
|
||||
document.getElementById('modalSeverityRow').style.display = 'flex';
|
||||
const badge = document.getElementById('modalSeverity');
|
||||
badge.textContent = result.severity.severity;
|
||||
badge.style.background = result.severity.color;
|
||||
} else {
|
||||
document.getElementById('modalSeverityRow').style.display = 'none';
|
||||
}
|
||||
|
||||
// 3. Chú thích màu sắc (Legend) trong modal
|
||||
const legendContainer = document.getElementById('modalLegendContainer');
|
||||
if (result.segmentation && result.segmentation.performed && result.segmentation.color_legend) {
|
||||
legendContainer.style.display = 'block';
|
||||
renderModalLegend(result.segmentation.color_legend);
|
||||
} else {
|
||||
legendContainer.style.display = 'none';
|
||||
}
|
||||
|
||||
// Hiển thị modal
|
||||
modal.classList.add('active');
|
||||
}
|
||||
|
||||
function renderModalLegend(colorLegend) {
|
||||
const itemsContainer = document.getElementById('modalLegendItems');
|
||||
itemsContainer.innerHTML = '';
|
||||
|
||||
colorLegend.forEach(item => {
|
||||
const isHighlight = item.key === 'effusion' || item.key === 'synovium';
|
||||
const legendItem = document.createElement('div');
|
||||
legendItem.className = 'legend-item';
|
||||
legendItem.innerHTML = `
|
||||
<div class="legend-color ${isHighlight ? 'legend-highlight' : ''}"
|
||||
style="background-color: rgb(${item.color.join(',')})"></div>
|
||||
<span>${item.name}</span>
|
||||
`;
|
||||
itemsContainer.appendChild(legendItem);
|
||||
});
|
||||
}
|
||||
|
||||
function closeResultPopup() {
|
||||
document.getElementById('resultModal').classList.remove('active');
|
||||
}
|
||||
|
||||
// Event Listeners for Modal
|
||||
document.getElementById('closeModal').addEventListener('click', closeResultPopup);
|
||||
document.getElementById('modalViewDetail').addEventListener('click', closeResultPopup);
|
||||
|
||||
// Click outside to close
|
||||
document.getElementById('resultModal').addEventListener('click', (e) => {
|
||||
if (e.target.id === 'resultModal') closeResultPopup();
|
||||
});
|
||||
|
||||
// Health check
|
||||
window.addEventListener('load', async () => {
|
||||
try {
|
||||
const response = await fetch(`${API_BASE}/api/health`);
|
||||
const health = await response.json();
|
||||
console.log('✅ Server ready:', health);
|
||||
} catch (error) {
|
||||
showError('Không thể kết nối server. Vui lòng khởi động FastAPI backend.');
|
||||
}
|
||||
});
|
||||
@@ -0,0 +1,16 @@
|
||||
--extra-index-url https://download.pytorch.org/whl/cpu
|
||||
|
||||
fastapi==0.135.1
|
||||
numpy==2.2.6
|
||||
opencv-python==4.13.0.92
|
||||
pillow==12.1.1
|
||||
python-multipart==0.0.22
|
||||
segmentation-models-pytorch==0.5.0
|
||||
timm==1.0.25
|
||||
torch==2.5.1
|
||||
torchvision==0.20.1
|
||||
torchaudio==2.5.1
|
||||
torchinfo==1.8.0
|
||||
tqdm==4.67.3
|
||||
uvicorn==0.41.0
|
||||
fpdf2==2.8.7
|
||||
@@ -0,0 +1,82 @@
|
||||
# VKIST Ultrasound
|
||||
|
||||
## Introduction
|
||||
**VKIST Ultrasound** is an application designed to support the diagnosis of **knee arthritis** using **knee ultrasound images**.
|
||||
The system processes ultrasound images and assists clinicians in identifying potential signs of arthritis, providing a supportive tool for medical analysis and research.
|
||||
|
||||
---
|
||||
|
||||
## Yêu cầu hệ thống
|
||||
|
||||
### 1. YÊU CẦU VỀ MÁY TÍNH
|
||||
* **Hệ điều hành:** Windows 10/11 (64-bit) hoặc Ubuntu 20.04/22.04.
|
||||
* **CPU:** Tối thiểu 4 nhân (khuyến nghị Intel Core i5 thế hệ 10 trở lên hoặc tương đương).
|
||||
* **RAM:** Tối thiểu 16GB.
|
||||
* **GPU:** NVIDIA GPU hỗ trợ CUDA (Kiến trúc Pascal trở lên, ví dụ: GTX 10-series, RTX series).
|
||||
* **VRAM:** Khuyến nghị 8GB trở lên để tối ưu tốc độ phân vùng (segmentation).
|
||||
|
||||
### 2. CÀI ĐẶT PHẦN MỀM HỖ TRỢ
|
||||
* **Quản lý môi trường:** [Anaconda3](https://www.anaconda.com/download) hoặc Miniconda.
|
||||
* **Đồ họa:** NVIDIA Driver tương thích với CUDA 12.4.
|
||||
* **CUDA Toolkit:** Phiên bản 12.4.
|
||||
* **Trình biên dịch C++:**
|
||||
* **Windows:** [Microsoft Visual C++ Build Tools](https://visualstudio.microsoft.com/visual-cpp-build-tools/).
|
||||
* **Ubuntu:** Cài đặt qua lệnh `sudo apt install build-essential`.
|
||||
|
||||
---
|
||||
|
||||
## Installation
|
||||
|
||||
### 1. Clone the Repository
|
||||
```bash
|
||||
git clone https://github.com/itvkist/vkist-ultrasound.git
|
||||
cd vkist-ultrasound
|
||||
```
|
||||
|
||||
### 2. Create Environment and Install Dependencies
|
||||
```bash
|
||||
conda create -n vkist-ultrasound python=3.10 -y
|
||||
conda activate vkist-ultrasound
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
### Download Model Weights
|
||||
|
||||
The weights of the models can be found in the following link:
|
||||
|
||||
```
|
||||
https://drive.google.com/drive/folders/1lBkplP-5uv6V2wR1CJ2COaGy1SnZxxJl
|
||||
```
|
||||
|
||||
After downloading the link, unzip and copy the files into the `./models` folder
|
||||
|
||||
### Run the Application
|
||||
|
||||
Start the application with:
|
||||
|
||||
```bash
|
||||
python app.py
|
||||
```
|
||||
|
||||
The application will be available at:
|
||||
|
||||
```
|
||||
http://localhost:8000
|
||||
```
|
||||
|
||||
### Notes
|
||||
|
||||
- Make sure your GPU drivers are compatible with the CUDA version installed.
|
||||
- If GPU support is not required, the PyTorch CPU version can also be used.
|
||||
|
||||
## Rules Section
|
||||
|
||||
### Naming Convention Exception for LEGACY Code
|
||||
- Legacy code (deprecated or intended for migration) may be referenced or used in the codebase only when accompanied by a conventional comment indicating its legacy status (e.g., `# LEGACY: to be refactored` or `# LEGACY: deprecated`).
|
||||
- Such legacy code should not be extended for new features; it is intended for read/reference only or as a stepping stone toward modernization.
|
||||
- When introducing new modules or files, follow the standard naming convention (e.g., snake_case for Python files, PascalCase for classes, etc.). Legacy exceptions are exempt only when explicitly marked.
|
||||
|
||||
### Visualization Guidelines
|
||||
- Prefer text-based diagram descriptions using PlantUML or Mermaid syntax for version control and diffability.
|
||||
- If a diagram must be drawn by hand, use raster image formats such as .png or .jpg.
|
||||
- Avoid binary formats like PDF, PPT, or other proprietary formats for diagrams in the repository.
|
||||
@@ -0,0 +1,212 @@
|
||||
<!DOCTYPE html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="UTF-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
<title>Architecture Review: VKIST Ultrasound Codebase</title>
|
||||
<script src="https://cdn.tailwindcss.com"></script>
|
||||
<script>
|
||||
tailwind.config = {
|
||||
theme: {
|
||||
extend: {
|
||||
colors: {
|
||||
primary: '#2563eb',
|
||||
secondary: '#64748b'
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
</script>
|
||||
<script src="https://cdn.jsdelivr.net/npm/mermaid@11/dist/mermaid.min.js"></script>
|
||||
<script>
|
||||
mermaid.initialize({
|
||||
startOnLoad: true,
|
||||
theme: 'neutral',
|
||||
});
|
||||
</script>
|
||||
</head>
|
||||
<body class="bg-gray-50 text-gray-800 antialiased p-6 md:p-12">
|
||||
<main class="max-w-5xl mx-auto">
|
||||
|
||||
<header class="mb-8 border-b border-gray-200 pb-6">
|
||||
<h1 class="text-3xl font-extrabold text-gray-900 mb-2">Architecture Review: VKIST Ultrasound Codebase</h1>
|
||||
<p class="text-lg text-gray-600">Identified deepening opportunities to improve testability and AI-navigability.</p>
|
||||
</header>
|
||||
|
||||
<article class="bg-white rounded-xl shadow-sm border border-gray-200 p-6 md:p-8 mb-8 transition-shadow hover:shadow-md">
|
||||
|
||||
<div class="flex flex-col md:flex-row md:items-center justify-between gap-4 mb-6">
|
||||
<h2 class="text-2xl font-bold text-gray-900">Candidate: Refactor app.py into modular services</h2>
|
||||
<div>
|
||||
<span class="px-3 py-1 rounded-full text-xs font-semibold uppercase tracking-wider bg-red-100 text-red-800">
|
||||
Priority: Strong
|
||||
</span>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="space-y-4 mb-6">
|
||||
<p class="text-sm bg-gray-50 p-3 rounded-lg border border-gray-100 text-gray-700">
|
||||
<strong class="text-gray-900">Files Affected:</strong> <code class="text-blue-600">app.py</code>
|
||||
<span class="text-gray-400 mx-2">→</span>
|
||||
<strong class="text-gray-900">Proposed:</strong>
|
||||
<code class="bg-white px-1.5 py-0.5 rounded border text-xs">inference_pipeline.py</code>,
|
||||
<code class="bg-white px-1.5 py-0.5 rounded border text-xs">model_loader.py</code>,
|
||||
<code class="bg-white px-1.5 py-0.5 rounded border text-xs">preprocessor.py</code>,
|
||||
<code class="bg-white px-1.5 py-0.5 rounded border text-xs">postprocessor.py</code>
|
||||
</p>
|
||||
|
||||
<div>
|
||||
<h3 class="text-sm font-semibold uppercase tracking-wider text-gray-500 mb-1">The Problem</h3>
|
||||
<p class="text-gray-700 leading-relaxed">
|
||||
The main application file (<code class="bg-gray-100 px-1 rounded text-sm text-red-600">app.py</code>) is acting as a "god object". It manages web framework setup, model loading, inference pipelines, preprocessing, postprocessing, utilities, and API endpoints simultaneously. This creates **low architectural depth** (the interface is nearly as complex as its implementation), **poor testability** (isolated pipeline units cannot be targeted), and **low locality** (unrelated logic must be parsed to fix individual features).
|
||||
</p>
|
||||
</div>
|
||||
|
||||
<div>
|
||||
<h3 class="text-sm font-semibold uppercase tracking-wider text-gray-500 mb-1">Proposed Solution</h3>
|
||||
<p class="text-gray-700 mb-2">Split operational concerns into highly focused, cleanly encapsulated modules:</p>
|
||||
<ul class="space-y-2 text-gray-700 pl-4 list-disc">
|
||||
<li><strong><code class="text-gray-900">ModelLoader</code>:</strong> Handles asynchronous loading and persistent caching of PyTorch weights (angle, inflammation, segmentation models).</li>
|
||||
<li><strong><code class="text-gray-900">Preprocessor</code>:</strong> Isolates structural image transformations and CLAHE enhancement parameters.</li>
|
||||
<li><strong><code class="text-gray-900">InferencePipeline</code>:</strong> Orchestrates execution flows: angle classification <span class="text-gray-400">→</span> inflammation detection <span class="text-gray-400">→</span> conditional segmentation <span class="text-gray-400">→</span> sizing/severity metrics.</li>
|
||||
<li><strong><code class="text-gray-900">Postprocessor</code>:</strong> Houses structural thickness evaluations, severity metrics calculations, and visual overlay generators.</li>
|
||||
<li><strong><code class="text-gray-900">APIHandler</code>:</strong> A thin, declarative FastAPI endpoint wrapper delegating exclusively to the primary <code class="text-sm">InferencePipeline</code>.</li>
|
||||
</ul>
|
||||
</div>
|
||||
|
||||
<div>
|
||||
<h3 class="text-sm font-semibold uppercase tracking-wider text-gray-500 mb-1">Expected Architecture Benefits</h3>
|
||||
<ul class="grid grid-cols-1 md:grid-cols-2 gap-3 text-gray-700">
|
||||
<li class="bg-blue-50/50 p-3 rounded-lg border border-blue-100">
|
||||
<strong class="text-blue-900 block mb-0.5">Architectural Depth</strong> High complexity is hidden beneath simple interfaces (e.g., <code class="text-xs">analyze_image(img) -> Result</code>).
|
||||
</li>
|
||||
<li class="bg-blue-50/50 p-3 rounded-lg border border-blue-100">
|
||||
<strong class="text-blue-900 block mb-0.5">High Code Locality</strong> Bugs in sizing algorithms map cleanly to the postprocessor without risking model caching logic.
|
||||
</li>
|
||||
<li class="bg-blue-50/50 p-3 rounded-lg border border-blue-100">
|
||||
<strong class="text-blue-900 block mb-0.5">Simplified Testing</strong> Individual pipeline states and weights can be unit-tested seamlessly using mock behaviors.
|
||||
</li>
|
||||
<li class="bg-blue-50/50 p-3 rounded-lg border border-blue-100">
|
||||
<strong class="text-blue-900 block mb-0.5">AI and Dev Navigation</strong> Highly categorized file trees accelerate context gathering for language models and onboarding engineers.
|
||||
</li>
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="grid grid-cols-1 xl:grid-cols-2 gap-6 mt-8">
|
||||
<div class="border border-gray-200 rounded-xl p-4 bg-gray-50">
|
||||
<h4 class="text-base font-bold text-gray-800 mb-3 flex items-center gap-2">
|
||||
<span class="w-2.5 h-2.5 rounded-full bg-red-500"></span> Before (Shallow Architecture)
|
||||
</h4>
|
||||
<div class="mermaid bg-white p-4 rounded-lg border border-gray-100 shadow-inner overflow-x-auto">
|
||||
graph TD
|
||||
A[FastAPI Endpoint] --> B[load_angle_model]
|
||||
A --> C[load_inflammation_model]
|
||||
A --> D[load_segmentation_model_sup]
|
||||
A --> E[load_segmentation_model_post]
|
||||
A --> F[predict_angle]
|
||||
A --> G[predict_inflammation]
|
||||
A --> H[segment_image]
|
||||
A --> I[measure_thickness_new]
|
||||
A --> J[analyze_inflammation_severity]
|
||||
A --> K[create_segmentation_overlay]
|
||||
A --> L[apply_clahe]
|
||||
A --> M[save_patient_data]
|
||||
|
||||
style A fill:#f87171,stroke:#ef4444,stroke-width:2px,color:#fff
|
||||
style B fill:#fee2e2,stroke:#f87171
|
||||
style C fill:#fee2e2,stroke:#f87171
|
||||
style D fill:#fee2e2,stroke:#f87171
|
||||
style E fill:#fee2e2,stroke:#f87171
|
||||
style F fill:#fee2e2,stroke:#f87171
|
||||
style G fill:#fee2e2,stroke:#f87171
|
||||
style H fill:#fee2e2,stroke:#f87171
|
||||
style I fill:#fee2e2,stroke:#f87171
|
||||
style J fill:#fee2e2,stroke:#f87171
|
||||
style K fill:#fee2e2,stroke:#f87171
|
||||
style L fill:#fee2e2,stroke:#f87171
|
||||
style M fill:#fee2e2,stroke:#f87171
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="border border-gray-200 rounded-xl p-4 bg-gray-50">
|
||||
<h4 class="text-base font-bold text-gray-800 mb-3 flex items-center gap-2">
|
||||
<span class="w-2.5 h-2.5 rounded-full bg-green-500"></span> After (Deepened Architecture)
|
||||
</h4>
|
||||
<div class="mermaid bg-white p-4 rounded-lg border border-gray-100 shadow-inner overflow-x-auto">
|
||||
graph TD
|
||||
subgraph API [API Gateway Router]
|
||||
A[FastAPI Endpoint] --> B[InferencePipeline.analyze]
|
||||
end
|
||||
|
||||
subgraph InferencePipeline [Core Workflow Orchestrator]
|
||||
B --> C[ModelLoader.get_angle_model]
|
||||
B --> D[Preprocess]
|
||||
B --> E[predict_angle]
|
||||
E --> F{Inflammation?}
|
||||
F -->|Yes| G[ModelLoader.get_inflammation_model]
|
||||
G --> H[predict_inflammation]
|
||||
H --> I{Inflammation Detected?}
|
||||
I -->|Yes| J[ModelLoader.get_seg_model_sup/post]
|
||||
J --> K[segment_image]
|
||||
K --> L[measure_thickness]
|
||||
K --> M[analyze_severity]
|
||||
L --> N[Result: Measurement]
|
||||
M --> O[Result: Severity]
|
||||
K --> P[Result: Segmentation Masks]
|
||||
E -->|No| Q[Result: Angle Only]
|
||||
F -->|No| Q
|
||||
end
|
||||
|
||||
subgraph ModelLoader [Model Assets Lifecycle]
|
||||
C --> C1[Load Angle Model]
|
||||
G --> G1[Load Inflammation Model]
|
||||
J --> J1[Load Segmentation Model]
|
||||
end
|
||||
|
||||
subgraph Preprocessor [Image Engineering]
|
||||
D --> D1[Transforms]
|
||||
D --> D2[CLAHE]
|
||||
end
|
||||
|
||||
subgraph Postprocessor [Metrics & Artifact Generation]
|
||||
L --> L1[Thickness Calc]
|
||||
M --> M1[Severity Scoring]
|
||||
end
|
||||
|
||||
style A fill:#3b82f6,stroke:#2563eb,stroke-width:2px,color:#fff
|
||||
style B fill:#dbeafe,stroke:#60a5fa
|
||||
style C fill:#dbeafe,stroke:#60a5fa
|
||||
style D fill:#dbeafe,stroke:#60a5fa
|
||||
style E fill:#dbeafe,stroke:#60a5fa
|
||||
style F fill:#dbeafe,stroke:#60a5fa
|
||||
style G fill:#dbeafe,stroke:#60a5fa
|
||||
style H fill:#dbeafe,stroke:#60a5fa
|
||||
style I fill:#dbeafe,stroke:#60a5fa
|
||||
style J fill:#dbeafe,stroke:#60a5fa
|
||||
style K fill:#dbeafe,stroke:#60a5fa
|
||||
style L fill:#dbeafe,stroke:#60a5fa
|
||||
style M fill:#dbeafe,stroke:#60a5fa
|
||||
style N fill:#e0f2fe,stroke:#0284c7
|
||||
style O fill:#e0f2fe,stroke:#0284c7
|
||||
style P fill:#e0f2fe,stroke:#0284c7
|
||||
style Q fill:#e0f2fe,stroke:#0284c7
|
||||
style C1 fill:#f3e8ff,stroke:#a855f7
|
||||
style G1 fill:#f3e8ff,stroke:#a855f7
|
||||
style J1 fill:#f3e8ff,stroke:#a855f7
|
||||
style D1 fill:#fef3c7,stroke:#d97706
|
||||
style D2 fill:#fef3c7,stroke:#d97706
|
||||
style L1 fill:#ecfccb,stroke:#65a30d
|
||||
style M1 fill:#ecfccb,stroke:#65a30d
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
</article>
|
||||
|
||||
<footer class="text-center text-sm text-gray-500 mt-12">
|
||||
<p>Total candidates identified: <span class="font-semibold text-gray-700">1</span></p>
|
||||
</footer>
|
||||
</main>
|
||||
</body>
|
||||
</html>
|
||||
@@ -7,16 +7,17 @@ Orchestrates API routers, role checks, Socratic circuit-breaker state evaluation
|
||||
Core Backend Team
|
||||
|
||||
## Boundary
|
||||
FastAPI server, API routers, authentication middleware, circuit breaker engine, report generator, RAG coordinator, ledger logger, and connections to Postgres, S3, Redis, Triton, Qdrant, ladybugDB.
|
||||
FastAPI server, API routers, authentication middleware, circuit breaker engine, report generator, RAG coordinator, RAG-Referee (BERT), ledger logger, and connections to Postgres + pgvector, S3 (MinIO), Redis, Triton, ladybugDB.
|
||||
|
||||
## Internal Design
|
||||
- Built with FastAPI (Python) and Uvicorn for async HTTP server.
|
||||
- Authentication middleware validates JWT tokens and enforces RBAC (roles: RO_RADIOLOGIST, RO_THERAPIST).
|
||||
- Socratic circuit-breaker engine monitors interaction telemetry (hover duration, decision time, override magnitude) and triggers safety dialogs.
|
||||
- Clinical Report Engine uses ReportLab to generate bilingual PDF reports per Circular 46/2018/TT-BYT.
|
||||
- RAG Coordinator orchestrates Retrieval-Augmented Generation: dense vector lookup in Qdrant, graph traversal in ladybugDB, prompt enrichment, LLM generation on Triton (PhoGPT/MedGemma), and hallucination guarding.
|
||||
- RAG Coordinator orchestrates Retrieval-Augmented Generation: dense vector lookup in pgvector (PostgreSQL HNSW), graph traversal in ladybugDB, mandatory pre-generation retrieval, prompt enrichment, LLM generation on browser WebLLM (GemmaE2B) or cloud Vertex AI (MedGemma via NFR-16a), and hallucination guarding via BERT RAG-Referee.
|
||||
- NLP Scrubber (Microsoft Presidio): re-verifies client edge redaction, refines residual PII, and returns error if unresolvable.
|
||||
- Ledger Logger appends immutable, cryptographically chained audit logs to Postgres via triggers preventing UPDATE/DELETE.
|
||||
- Connections: Postgres (via SQLAlchemy), S3 (via boto3), Redis (via redis-py), Triton (via gRPC), Qdrant (via gRPC/HTTP), ladybugDB (via in-process C++ bindings).
|
||||
- Connections: Postgres + pgvector (via SQLAlchemy), S3 (via boto3), Redis (via redis-py), Triton (via gRPC — CV + EmbeddingGemma only), ladybugDB (via in-process C++ bindings).
|
||||
- Model weights loaded at startup from internal registry; cached in memory.
|
||||
- API endpoints layered: public clinical (sessions, analysis, reports, feedback) and internal/local safety (explanations, safety, drift, RAG, activations, annotations, ground-truth, escalation, morphology, telemetry).
|
||||
|
||||
@@ -11,10 +11,10 @@ Qdrant vector database instances, ladybugDB graph database instances, embedding
|
||||
|
||||
## Internal Design
|
||||
- Hybrid knowledge architecture: vector similarity search + graph traversal
|
||||
- Qdrant: stores guideline section embeddings (BioClinicalBERT, PubMedBERT) with payload metadata
|
||||
- pgVec: stores guideline section embeddings (BioClinicalBERT, PubMedBERT) with payload metadata
|
||||
- ladybugDB: stores ontology concepts (SNOMED-CT, LOINC, RadLex) and relational axioms
|
||||
- EmbeddingGemma: generates 768-dimension vectors for text chunks
|
||||
- PhoGPT/MedGPT: LLM for answer generation with constrained decoding
|
||||
- GemmaE2B/MedGemma: LLM for answer generation with constrained decoding
|
||||
- Retrieval pipeline: hybrid search (vector + BM25) → graph expansion → reranking
|
||||
- Grounding module: verifies LLM outputs against source guidelines with citation extraction
|
||||
- Arbitration engine: resolves conflicting evidence using belief propagation
|
||||
Submodule workspace/sprint_1_2/Design_Material/LEGACY/VKIST_ML/codebase-vkist-ultrasound-legacy deleted from 8b2758928a
570
workspace/sprint_1_2/SPRINT_1_2_ARCHITECTURE_SPEC.md
Normal file
570
workspace/sprint_1_2/SPRINT_1_2_ARCHITECTURE_SPEC.md
Normal file
@@ -0,0 +1,570 @@
|
||||
# Sprint 1-2 Architecture Specification
|
||||
|
||||
**Sprint:** Sprint 1 (June 15 – June 26) + Sprint 2 (June 29 – July 10)
|
||||
**Theme:** "The Fast PoC Baseline" + "Multi-Modal & NLP Integration"
|
||||
**Parent Document:** [SOLUTION_ARCHITECTURE_SPEC.md](../SOLUTION_ARCHITECTURE_SPEC.md)
|
||||
|
||||
---
|
||||
|
||||
## 1. Sprint Scope & Goals
|
||||
|
||||
### 1.1 Sprint 1: The Fast PoC Baseline
|
||||
- Establish interactive end-to-end processing pipeline
|
||||
- Rapid UI prototype with high-fidelity mockups
|
||||
- Configure initial FastAPI server pipelines to process raw array matrices
|
||||
- Output early inference mask previews onto browser-based preview window canvas
|
||||
- Wire ultrasound image ingest pathways into classification pipeline
|
||||
|
||||
### 1.2 Sprint 2: Multi-Modal & NLP Integration
|
||||
- Embed localized NLP translation modules
|
||||
- Implement client-side privacy scrubbing masks for Decree 13 compliance
|
||||
- Configure on-device string parsers to sanitize personal identification tokens
|
||||
- Deploy multi-modal diagnostic chat system
|
||||
- Build zero-friction NLP conduits for clinical abbreviations
|
||||
|
||||
---
|
||||
|
||||
## 2. Architectural Constraints for Sprint 1-2
|
||||
|
||||
| Constraint | Implementation |
|
||||
| --- | --- |
|
||||
| **Air-Gapped Hospital LAN** | No external cloud processing during normal operation. All inference and storage on-premise via K3s. NFR-16a governs emergency cloud fallback only. |
|
||||
| **Code & Issue Hosting (NFR-16a exception)** | GitLab CE and Jira run self-hosted on a cloud VM (not SaaS). All code, tickets, and pipeline configs transit over public internet to reach the VM. Compensating controls: no PHI in commits or tickets (pre-push hook + team policy); SSH-only git access with certificate pinning; daily GitLab RDB backup to hospital MinIO (not cloud storage); cloud VM IAM with minimum-role access and 2FA; VM access restricted to hospital whitelisted IPs. This exception is reviewed at PoC sign-off. |
|
||||
| **Network Bandwidth (10 Mbps max)** | Optimize payload sizes, leverage local caching (IndexedDB, Redis). |
|
||||
| **DICOM Compliance** | Process DICOM images via pydicom stream over local HTTPS. |
|
||||
| **Client Memory ≤ 150 MB** | PWA uses React + Zustand + LiteRT (no dedicated GPU required). |
|
||||
| **Latency ≤ 1.5s for inference** | Heavy ML offloaded to Triton Inference Server with ONNX/TensorRT. |
|
||||
| **Zero PHI Leakage** | Client-side scrubbing before network transfer; no PHI in Git/Jenkins. |
|
||||
|
||||
---
|
||||
|
||||
## 3. System Context Diagram (C4) - Sprint 1-2 Scope (FR-25 Synovitis Grading)
|
||||
|
||||
```plantuml
|
||||
@startuml "VKIST_MSK_Sprint1_2_Context_FR25"
|
||||
!include <C4/C4_Context>
|
||||
|
||||
title System Context - Sprint 1-2: FR-25 Synovitis Grading & NLP Integration
|
||||
' Sprint 1-2 scope: FR-25 only with NLP safety layer
|
||||
' Active Users: UP5 | Governance: UP1, UP4 | Future: UP6, UP7, UP8
|
||||
|
||||
' === ACTORS IN SPRINT SCOPE ===
|
||||
Person(radiologist, "Diagnostic Radiologist (UP5)", "FR-25: load scan, review AI grade (0-3), confirm/override, finalize & sign, view GradCAM, engage circuit breaker, review RAG evidence")
|
||||
|
||||
' === GOVERNANCE / NFR ALIGNMENT ===
|
||||
Person_Ext(senior_expert, "Healthcare Senior Expert (UP1)", "Governance: clinical protocol validation, MOH guideline approval, model threshold sign-off")
|
||||
Person_Ext(support_staff, "Support Staff (UP4)", "Registration, case queue management")
|
||||
|
||||
' === FUTURE SPRINTS ===
|
||||
Person_Ext(therapist, "Physical Therapist (UP6)", "Future sprint: prescription parser, 3D mapping, kinetic overlay, patient education")
|
||||
Person_Ext(ortho_surgeon, "Orthopedic Surgeon (UP7)", "Future sprint: treatment planning, aggregated dashboard, 3D anatomy education")
|
||||
Person_Ext(patient_caregiver, "Patient & Caregiver (UP8)", "Future sprint: self-monitoring portal, inflammation tracker, medication reminders")
|
||||
|
||||
Person(admin, "System Administrator", "K3s deployment, model updates, NGINX failover, Prometheus/Grafana monitoring")
|
||||
|
||||
' === SYSTEM ===
|
||||
System(mpps, "VKIST MSK Processing System\n(FR-25 Sprint 1-2)", "Knee ultrasound AI: angle→inflammation→segmentation\nGrading: synovitis 0-3 with GradCAM\nSafety: Circuit Breaker, BERT drift, RAG-Referee\nNLP: Decree 13 scrubbing, GemmaE2B/MedGemma explanations\nReporting: Circular 46 PDF")
|
||||
|
||||
' === EXTERNAL SYSTEMS IN SCOPE ===
|
||||
System_Ext(pacs, "Hospital PACS / Ultrasound", "DICOM source (C-MOVE + direct capture)")
|
||||
System_Ext(emr, "Hospital EMR/HIS", "Finalized reports, audit logs (HL7/FHIR)")
|
||||
System_Ext(triton, "Triton Inference Server", "GPU inference: angle, inflammation, segmentation + embeddings")
|
||||
System_Ext(knowledge, "Clinical Knowledge Stack", "ladybugDB (ontology), pgvector (MOH guideline vectors in Postgres HNSW), EmbeddingGemma (RAG embedding model), GemmaE2B (browser WebLLM), MedGemma (cloud Vertex AI)")
|
||||
|
||||
' === RELATIONSHIPS ===
|
||||
Rel(radiologist, mpps, "Load scan, review grade, confirm/override, finalize, view explanations, engage safety dialog", "HTTPS")
|
||||
Rel(admin, mpps, "Deploys, monitors, configures", "HTTPS/SSH")
|
||||
Rel(senior_expert, mpps, "Validates protocols, approves thresholds", "HTTPS")
|
||||
Rel(support_staff, mpps, "Registration, case queue", "HTTPS")
|
||||
|
||||
Rel(therapist, mpps, "Not in Sprint 1-2 scope", "—")
|
||||
Rel(ortho_surgeon, mpps, "Not in Sprint 1-2 scope", "—")
|
||||
Rel(patient_caregiver, mpps, "Not in Sprint 1-2 scope", "—")
|
||||
|
||||
Rel(mpps, pacs, "DICOM import", "C-MOVE + upload")
|
||||
Rel(mpps, emr, "Finalized reports, audit", "HL7/FHIR")
|
||||
Rel(mpps, triton, "ML inference", "gRPC :8001")
|
||||
Rel(mpps, knowledge, "RAG + ontology + LLM", "gRPC/HTTP + C++")
|
||||
|
||||
@enduml
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 4. Container Diagram (C4) - Sprint 1-2 Implementation
|
||||
|
||||
```plantuml
|
||||
@startuml "VKIST_MSK_Containers_Sprint1_2"
|
||||
!include <C4/C4_Container>
|
||||
|
||||
title Container Diagram - VKIST MSK Platform (Sprint 1-2)
|
||||
|
||||
Person(radiologist, "Radiologist (UP5)", "Performs primary diagnosis, view validation, and severity grading.")
|
||||
Person(therapist, "Physical Therapist (UP6)", "Observes scans, evaluates kinetic data, and structures physical rehabilitation plans.")
|
||||
|
||||
System_Boundary(hospital_lan, "Air-Gapped Hospital LAN (Max 10 Mbps)") {
|
||||
|
||||
Container(pwa, "React PWA Frontend", "React, TS, Zustand, LiteRT, MediaPipe, Dexie.js", "Interactive visualization, local WebAssembly inference for view-angle validation, encrypted IndexedDB cache, DICOM upload, edge guardrail (Transformers.js BERT + OpenRedaction + pii-filter) in dedicated WebWorkers")
|
||||
|
||||
Container(nginx, "NGINX Reverse Proxy + Keepalived", "NGINX 1.27, Keepalived", "SSL/TLS termination, single VIP, instant failover (<=2s), routes to active FastAPI node")
|
||||
|
||||
System_Boundary(backend_servers, "Application Server Cluster") {
|
||||
Container(fastapi, "FastAPI Application Server", "Python, Uvicorn, SQLAlchemy, ReportLab", "API orchestration, angle classification routing, inflammation detection, segmentation post-processing, measurement, severity analysis, PDF report generation, client-side scrubbing validation")
|
||||
|
||||
System_Boundary(ml_inference, "ML Inference Stack (Sprint 1-2)") {
|
||||
Container(triton, "Triton Inference Server", "NVIDIA Triton, ONNX, OpenVINO, TensorRT", "Executes 3-step ML pipeline: angle classification, inflammation detection, segmentation (UNet3+/DeepLabV3)")
|
||||
ContainerDb(model_store, "Local Model Store", "ONNX/Torch weights on disk", "Stores model checkpoints mounted read-only into Triton")
|
||||
}
|
||||
|
||||
System_Boundary(observability, "Observability (Sprint 1-2)") {
|
||||
Container(prometheus, "Prometheus", "Prometheus", "Scrapes FastAPI and Triton metrics")
|
||||
Container(grafana, "Grafana", "Grafana", "Operational dashboards for inference latency, GPU utilization, cache hit rate")
|
||||
}
|
||||
|
||||
System_Boundary(data_stack, "Data Stack (Sprint 1-2)") {
|
||||
ContainerDb(postgres, "PostgreSQL + PostGIS", "PostgreSQL", "Stores session metadata, EMR records, spatial markers, audit ledger")
|
||||
ContainerDb(redis, "Redis Cache", "Redis", "Session state, JWT tokens, rate limiting")
|
||||
ContainerDb(s3, "S3 Object Store (MinIO)", "MinIO", "DICOM-derived images, segmentation overlays, Grad-CAM heatmaps, report blobs")
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
Rel(radiologist, pwa, "Uploads DICOM, views overlays, accepts/rejects grades")
|
||||
Rel(therapist, pwa, "Reviews scan results, annotates feedback")
|
||||
|
||||
Rel(pwa, nginx, "HTTPS requests with encrypted DICOM payloads", "HTTPS (Port 443)")
|
||||
Rel(nginx, fastapi, "Routes API requests", "HTTP/1.1 (Port 8000)")
|
||||
|
||||
Rel(fastapi, redis, "Session verification, rate limiting", "TCP (Port 6379)")
|
||||
Rel(fastapi, postgres, "Metadata, audit ledger, spatial queries", "SQL/TCP (Port 5432)")
|
||||
Rel(fastapi, s3, "Image and report blob storage", "S3 API")
|
||||
|
||||
Rel(fastapi, triton, "Offloads ML inference: angle, inflammation, segmentation", "gRPC (Port 8001)")
|
||||
Rel(triton, model_store, "Reads model weights", "Local FS")
|
||||
|
||||
Rel(prometheus, fastapi, "Request latency, error rate, DB pool", "HTTP /metrics")
|
||||
Rel(prometheus, triton, "Model latency, VRAM, GPU utilization", "HTTP /metrics")
|
||||
Rel(grafana, prometheus, "Dashboard queries", "HTTP (Port 9090)")
|
||||
|
||||
@enduml
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 5. Component Diagram (C4) - FastAPI Server Internals (Sprint 1-2)
|
||||
|
||||
```plantuml
|
||||
@startuml "VKIST_MSK_Backend_Components_Sprint1_2"
|
||||
!include <C4/C4_Component>
|
||||
|
||||
title Component Diagram - FastAPI Application Server (Sprint 1-2)
|
||||
|
||||
Container_Boundary(backend, "FastAPI Application Server") {
|
||||
|
||||
System_Boundary(api_routers, "API Routers") {
|
||||
Component(analyze_router, "Analysis Router", "FastAPI Router", "/api/analyze - Main ML pipeline endpoint\nHandles angle model selection, inflammation detection, segmentation routing, measurement, severity analysis")
|
||||
Component(save_router, "Save Router", "FastAPI Router", "/api/save - Patient data persistence\nSanitizes metadata, stores images and PDF reports")
|
||||
Component(export_router, "PDF Export Router", "FastAPI Router", "/api/export-pdf - On-demand PDF generation")
|
||||
Component(health_router, "Health Router", "FastAPI Router", "/api/health - Liveness probe")
|
||||
}
|
||||
|
||||
System_Boundary(ml_pipeline, "ML Pipeline Components (Sprint 1-2)") {
|
||||
Component(angle_classifier, "Angle Classifier", "PyTorch/ONNX", "Classifies ultrasound plane (med-lat, post-trans, sup-trans-flex, sup-up-long)\nModels: ConvNeXt-Tiny, DenseNet-121, ResNet-50, EfficientNet-B2, Swin-V2-S")
|
||||
Component(inflammation_detector, "Inflammation Detector", "PyTorch/ONNX", "Binary classifier for inflammation presence\nModel: EfficientNet-B0 (2-class)")
|
||||
Component(segmentation_engine, "Segmentation Engine", "PyTorch/ONNX", "Pixel-wise anatomical segmentation\nModels: DeepLabV3-ResNet50/101, UNet-ResNet101, EfficientFeedback, UNet3Plus-Attention")
|
||||
Component(measurement_engine, "Measurement Engine", "Python/NumPy/OpenCV", "Calculates synovium thickness in mm, effusion metrics\nPIXEL_TO_MM = 45.0 / 655.0")
|
||||
Component(severity_analyzer, "Severity Analyzer", "Python", "Classifies synovitis grade (0-3) with combined effusion+synovium scoring\nGenerates Vietnamese clinical descriptions")
|
||||
Component(preprocessor, "Image Preprocessor", "Python/PIL/OpenCV", "CLAHE contrast enhancement, resize, normalize")
|
||||
Component(overlay_renderer, "Overlay Renderer", "Python/PIL", "Creates color-coded segmentation overlays with measurement annotations")
|
||||
}
|
||||
|
||||
System_Boundary(knowledge_stack, "Knowledge Stack (Sprint 2)") {
|
||||
Component(nlp_scrubber, "Patient Data Scrubber", "Microsoft Presidio", "Re-verify edge redaction; refine residual PII; error if unresolvable")
|
||||
Component(rag_coordinator, "RAG Coordinator", "Python", "Retrieves MOH guideline passages from pgvector (PostgreSQL HNSW index); mandatory pre-generation retrieval for all LLM tiers (browser WebLLM and cloud MedGemma)")
|
||||
Component(ontology_query, "Ontology Query Engine", "C++ bindings", "ladybugDB graph traversal for anatomical entity relationships")
|
||||
Component(guardrail_edge, "Edge Guardrail", "Transformers.js BERT", "Client-side hallucination/mal-intention/scope-breach scoring; triggers session termination + cloud mitigate via FastAPI guardrail-check endpoint")
|
||||
Component(referee, "RAG-Referee", "BERT classifier", "Server-side 3-axis citation contestant validation (attribution, cohesion, factual contestant status)")
|
||||
}
|
||||
|
||||
System_Boundary(reporting, "Reporting & Audit") {
|
||||
Component(pdf_generator, "PDF Report Generator", "ReportLab", "Bilingual (VI/EN) medical reports per Circular 46/2018/TT-BYT")
|
||||
Component(audit_logger, "Immutable Audit Logger", "SQLAlchemy/Postgres", "Append-only clinical decision and AI interaction logs\nPrevents UPDATE/DELETE via DB triggers")
|
||||
}
|
||||
|
||||
System_Boundary(infrastructure, "Infrastructure") {
|
||||
Component(cache_client, "Redis Client", "redis-py", "Session state, JWT tokens, rate limiting")
|
||||
Component(storage_client, "S3 Client", "boto3", "DICOM images, overlays, reports, Grad-CAM heatmaps")
|
||||
Component(db_client, "PostgreSQL Client", "SQLAlchemy", "Relational/spatial queries, audit ledger")
|
||||
Component(triton_client, "Triton gRPC Client", "gRPC", "Inference offloading for heavy ML models")
|
||||
}
|
||||
}
|
||||
|
||||
' Internal relationships
|
||||
Component(analyze_router, preprocessor, "Uploads image, applies CLAHE")
|
||||
Component(analyze_router, angle_classifier, "Classifies angle plane")
|
||||
Component(analyze_router, inflammation_detector, "Detects inflammation (if post-trans or sup-up-long)")
|
||||
Component(analyze_router, segmentation_engine, "Runs segmentation (if inflammation detected)")
|
||||
Component(analyze_router, measurement_engine, "Measures thickness (sup-up-long only)")
|
||||
Component(analyze_router, severity_analyzer, "Grades synovitis severity")
|
||||
Component(analyze_router, overlay_renderer, "Generates visualization overlay")
|
||||
|
||||
Component(save_router, nlp_scrubber, "Validates scrubbing before save")
|
||||
Component(save_router, pdf_generator, "Triggers PDF generation")
|
||||
Component(save_router, audit_logger, "Logs save action immutably")
|
||||
|
||||
Component(export_router, pdf_generator, "Generates on-demand PDF")
|
||||
|
||||
Component(rag_coordinator, triton_client, "RAG embedding extraction via Triton (EmbeddingGemma)")
|
||||
Component(rag_coordinator, ontology_query, "Graph traversal for evidence")
|
||||
Component(rag_coordinator, referee, "Mandatory RAG-Referee validation per axis")
|
||||
Component(guardrail_edge, "Edge Guardrail", "Transformers.js BERT", "Scores hallucination/mal-intention/scope-breach in WebWorker")
|
||||
Component(guardrail_edge, referee, "Violation signal → server-side referee gate")
|
||||
|
||||
' Infrastructure connections
|
||||
Component(analyze_router, cache_client, "Session validation")
|
||||
Component(analyze_router, storage_client, "Image/report I/O")
|
||||
Component(analyze_router, db_client, "Metadata, audit")
|
||||
Component(analyze_router, triton_client, "ML inference offload")
|
||||
|
||||
@enduml
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 6. Deployment Diagram (C4) - Sprint 1-2 Hospital LAN Runtime
|
||||
|
||||
```plantuml
|
||||
@startuml "VKIST_MSK_Deployment_Sprint1_2"
|
||||
!include <C4/C4_Deployment>
|
||||
|
||||
title Deployment Diagram - VKIST MSK Platform (Sprint 1-2)
|
||||
|
||||
Deployment_Node(hw, "Hospital On-Premise Hardware\n(Dell PowerEdge / Local Server)") {
|
||||
Deployment_Node(k8s_alt, "K3s Runtime (Sprint 1-2)") {
|
||||
Deployment_Node(nginx_node, "NGINX Container") {
|
||||
Container(nginx_c, "NGINX 1.27 + Keepalived", "Reverse proxy, SSL, VIP failover")
|
||||
}
|
||||
Deployment_Node(fastapi_node, "FastAPI Container") {
|
||||
Container(fastapi_c, "FastAPI Uvicorn", "Python app: analysis, save, export, NLP scrub, RAG")
|
||||
}
|
||||
Deployment_Node(prom_node, "Prometheus Container") {
|
||||
Container(prom_c, "Prometheus", "Metrics collection")
|
||||
}
|
||||
Deployment_Node(grafana_node, "Grafana Container") {
|
||||
Container(graf_c, "Grafana", "Dashboards")
|
||||
}
|
||||
}
|
||||
|
||||
Deployment_Node(vm_db, "Database / Storage VM") {
|
||||
Deployment_Node(pg_node, "PostgreSQL + pgvector Node") {
|
||||
ContainerDb(pg_c, "PostgreSQL + PostGIS + pgvector", "Primary DB, vector HNSW index, audit ledger")
|
||||
}
|
||||
Deployment_Node(redis_node, "Redis Node") {
|
||||
ContainerDb(redis_c, "Redis", "Session cache, rate limit")
|
||||
}
|
||||
Deployment_Node(s3_node, "MinIO Node") {
|
||||
ContainerDb(s3_c, "MinIO S3", "Object storage for images and reports")
|
||||
}
|
||||
}
|
||||
|
||||
Deployment_Node(triton_hw, "Inference Server (GPU Node)") {
|
||||
Deployment_Node(triton_node, "Triton Container") {
|
||||
Container(triton_c, "Triton Inference Server", "ONNX/TensorRT models, ensemble pipelines")
|
||||
}
|
||||
Deployment_Node(model_vol, "Model Volume") {
|
||||
ContainerDb(model_c, "Model Weights", "Read-only mount: .pth / .onnx files")
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
Deployment_Node(client, "Clinician Workstation / Tablet") {
|
||||
ContainerDb(browser, "Browser PWA", "React, Service Worker, IndexedDB")
|
||||
}
|
||||
|
||||
Rel(browser, nginx_c, "HTTPS (port 443)", "TLS 1.3")
|
||||
Rel(nginx_c, fastapi_c, "HTTP (port 8000)", "Internal LAN")
|
||||
Rel(fastapi_c, pg_c, "SQL/TCP (port 5432)", "LAN")
|
||||
Rel(fastapi_c, s3_c, "S3 API", "LAN")
|
||||
Rel(fastapi_c, triton_c, "gRPC (port 8001)", "LAN (or same host)")
|
||||
|
||||
Rel(prom_c, fastapi_c, "/metrics scrape", "HTTP")
|
||||
Rel(prom_c, triton_c, "/metrics scrape", "HTTP")
|
||||
Rel(graf_c, prom_c, "Dashboard", "HTTP")
|
||||
|
||||
Rel(triton_c, model_vol, "Loads weights", "Read-only FS")
|
||||
|
||||
Rel(browser, browser, "Local-only WebAssembly inference (LiteRT/MediaPipe)", "No network")
|
||||
|
||||
@enduml
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 7. Sprint 1-2 ML Workflow: Ultrasound Processing Pipeline
|
||||
|
||||
```plantuml
|
||||
@startuml "VKIST_MSK_ML_Pipeline_Sprint1_2"
|
||||
@startuml
|
||||
title ML Pipeline - Knee Ultrasound Analysis (Sprint 1-2)
|
||||
|
||||
start
|
||||
|
||||
:Clinician uploads DICOM via PWA;
|
||||
:PWA encrypts payload, sends over HTTPS;
|
||||
|
||||
:NGINX routes to active FastAPI node;
|
||||
|
||||
:FastAPI receives image;
|
||||
:Apply CLAHE preprocessing (contrast enhancement);
|
||||
|
||||
:RUN ANGLE CLASSIFICATION;
|
||||
:Model: ConvNeXt/DenseNet/ResNet/EfficientNet/Swin-V2;
|
||||
:Classes: med-lat, post-trans, sup-trans-flex, sup-up-long;
|
||||
|
||||
if (Angle class?) then (post-trans)
|
||||
: Inflammation Detection;
|
||||
:Model: EfficientNet-B0 (binary);
|
||||
if (Inflammation?) then (yes)
|
||||
:Post-Trans Segmentation;
|
||||
:Model: DeepLabV3-ResNet101;
|
||||
:Classes: fat, tendon, muscle, femur, artery, synovium, baker's cyst;
|
||||
:Generate segmentation masks;
|
||||
:Create overlay image;
|
||||
else (no)
|
||||
:Skip segmentation;
|
||||
:Set severity = 0;
|
||||
endif
|
||||
elseif (sup-up-long)
|
||||
: Inflammation Detection;
|
||||
if (Inflammation?) then (yes)
|
||||
:Suprapatellar Segmentation;
|
||||
:Model: UNet3Plus-Attention / DeepLabV3 / EfficientFeedback;
|
||||
:Classes: effusion, fat, fat-pat, femur, synovium, tendon;
|
||||
:Generate segmentation masks;
|
||||
|
||||
:Measure Synovium Thickness;
|
||||
:ROI: middle 1/3 of bounding box;
|
||||
:Unit: mm (PIXEL_TO_MM = 45/655);
|
||||
|
||||
:Create overlay with measurement annotations;
|
||||
|
||||
:Analyze Severity;
|
||||
:Combined score: effusion (60%) + synovium (40%);
|
||||
:Grade: 0 (Rất nhẹ) / 1 (Nhẹ) / 2 (Trung bình) / 3 (Nặng);
|
||||
else (no)
|
||||
:Skip segmentation and measurement;
|
||||
:Set severity = 0;
|
||||
endif
|
||||
else (other angles)
|
||||
:Only angle classification result returned;
|
||||
endif
|
||||
|
||||
:Generate enhanced image (CLAHE);
|
||||
:Assemble JSON response with overlays, masks, measurements, severity;
|
||||
|
||||
if (Save requested?) then (yes)
|
||||
:NLP Scrubber validates Decree 13 compliance;
|
||||
:Store images to MinIO S3;
|
||||
:Store metadata to PostgreSQL;
|
||||
:Append immutable audit log;
|
||||
:Generate bilingual PDF report;
|
||||
endif
|
||||
|
||||
:Return response to PWA;
|
||||
|
||||
stop
|
||||
|
||||
@enduml
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 8. Data Flow Diagram (Activity View)
|
||||
|
||||
```plantuml
|
||||
@startuml "VKIST_MSK_DataFlow_Sprint1_2"
|
||||
title Data Flow - VKIST MSK Platform (Sprint 1-2)
|
||||
|
||||
|PWA Client|
|
||||
start
|
||||
:Upload DICOM image;
|
||||
:Apply CLAHE preprocessing locally;
|
||||
:Encrypt payload;
|
||||
:Send HTTPS POST to /api/analyze;
|
||||
|
||||
|NGINX Gateway|
|
||||
:Receive request;
|
||||
:SSL termination;
|
||||
:Route to active FastAPI node;
|
||||
|
||||
|FastAPI Server|
|
||||
:Receive image;
|
||||
:Validate JWT + RBAC;
|
||||
:Load angle classifier model;
|
||||
:Run angle classification;
|
||||
if (Angle class?) then (post-trans)
|
||||
:Load inflammation detector;
|
||||
:Run inflammation detection;
|
||||
if (Detected?) then (yes)
|
||||
:Load post-trans segmentation model;
|
||||
:Run DeepLabV3-ResNet101 inference via Triton;
|
||||
:Receive masks from Triton;
|
||||
:Create segmentation overlay;
|
||||
:Return JSON with overlay + masks;
|
||||
else (no)
|
||||
:Return JSON with angle + inflammation only;
|
||||
endif
|
||||
elseif (sup-up-long)
|
||||
:Load inflammation detector;
|
||||
:Run inflammation detection;
|
||||
if (Detected?) then (yes)
|
||||
:Load suprapatellar segmentation model;
|
||||
:Run UNet3Plus-Attention inference via Triton;
|
||||
:Receive masks from Triton;
|
||||
:Measure synovium thickness (ROI middle 1/3);
|
||||
:Calculate effusion metrics;
|
||||
:Analyze severity (0-3 combined score);
|
||||
:Create overlay with measurement annotations;
|
||||
:Return JSON with overlay + measurement + severity;
|
||||
else (no)
|
||||
:Return JSON with angle + inflammation only;
|
||||
endif
|
||||
else (other)
|
||||
:Return angle classification only;
|
||||
endif
|
||||
|
||||
|PWA Client|
|
||||
:Receive JSON response;
|
||||
:Display enhanced image + overlays;
|
||||
:Render measurement annotations;
|
||||
:Show severity grade with color coding;
|
||||
|
||||
if (User action: Save?) then (yes)
|
||||
|PWA Client|
|
||||
:Run NLP scrubber (Decree 13);
|
||||
:Remove PII from metadata;
|
||||
:Send HTTPS POST to /api/save;
|
||||
|
||||
|FastAPI Server|
|
||||
:Validate scrubbing compliance;
|
||||
:Store images to MinIO S3;
|
||||
:Store metadata to PostgreSQL;
|
||||
:Append immutable audit log;
|
||||
:Generate bilingual PDF report (Circular 46);
|
||||
:Return success + folder path;
|
||||
|
||||
|PWA Client|
|
||||
:Confirm save to user;
|
||||
endif
|
||||
|
||||
stop
|
||||
|
||||
@enduml
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 9. Non-Functional Requirements Coverage (Sprint 1-2)
|
||||
|
||||
| NFR | Sprint 1-2 Implementation |
|
||||
| --- | --- |
|
||||
| **NFR-1** (DICOM Speed ≤ 3.0s) | Local Triton inference + Redis caching of recent sessions |
|
||||
| **NFR-4** (Client Memory ≤ 150 MB) | React PWA with Zustand; LiteRT WebAssembly (no GPU); Dexie.js for local cache |
|
||||
| **NFR-5** (Inference ≤ 1.5s) | Triton with TensorRT/OpenVINO quantization; ONNX runtime |
|
||||
| **NFR-6** (VRAM ≤ 2 GB) | Quantized ONNX models; batch size 1; DeepLabV3-ResNet101 optimized |
|
||||
| **NFR-7** (UI Refresh ≤ 200ms) | Token streaming via SSE; frontend state updates async |
|
||||
| **NFR-8** (Fault Tolerance) | IndexedDB local cache; service worker offline mode; session recovery |
|
||||
| **NFR-9** (Availability ≥ 99.9%) | NGINX + Keepalived active-passive VIP; Docker restart policies |
|
||||
| **NFR-10** (Generative Safety) | Edge guardrail: prompt rules + BERT detection (hallucination/mal-intention/scope-breach) → session termination → cloud mitigate (Vertex AI). Mandatory RAG pre-processing for all LLM tiers (not optional tool calling). RAG-Referee citation contestant validation (3-axis). Server-side Decree 13 redaction ground-check before Vertex AI egress. |
|
||||
| **NFR-11** (Onboarding ≤ 45 min) | High-fidelity Figma prototypes in Sprint 1; structured workflows |
|
||||
| **NFR-13** (Grad-CAM Zero-Click) | Overlay rendered directly on upload; zero extra UI steps |
|
||||
| **NFR-14** (No client GPU) | PWA falls back to CPU-bound rendering; LiteRT for lightweight inference |
|
||||
| **NFR-16** (Air-Gapped) | Entire inference and storage stack on-premise via K3s; no external cloud for clinical data. NFR-16a exception: GitLab/Jira on cloud VM with compensating controls. |
|
||||
| **NFR-17** (Immutable Audit) | PostgreSQL triggers prevent UPDATE/DELETE on audit tables |
|
||||
| **NFR-18** (RAG Citations) | pgvector retrieves MOH guideline passages (PostgreSQL HNSW index); LLM generates footnoted explanations |
|
||||
| **NFR-19** (HITL Gate) | Database state machine prevents FINALIZED status without clinician digital signature |
|
||||
|
||||
---
|
||||
|
||||
## 10. Key Decree 13 / Circular 46 Compliance (Sprint 2)
|
||||
|
||||
### 10.1 Decree 13/2023/ND-CP - Personal Data Protection
|
||||
- Client-side scrubbing via `nlp_scrubber` component before any network transfer
|
||||
- Regex-based PII removal (names, IDs, phone numbers) in browser
|
||||
- No PHI stored in Git, Jenkins artifacts, or external systems
|
||||
|
||||
### 10.2 Circular 46/2018/TT-BYT - EMR Compliance
|
||||
- PDF reports generated per official MOH format (bilingual VI/EN)
|
||||
- Audit trail immutable via database triggers
|
||||
- Reports stored with cryptographic checksums in MinIO
|
||||
|
||||
---
|
||||
|
||||
## 11. Infrastructure Decisions (Sprint 1-2)
|
||||
|
||||
| Decision | Choice | Rationale |
|
||||
| --- | --- | --- |
|
||||
| **Orchestration** | K3s (Kubernetes-certified, lightweight edge distribution) | Chosen over Docker Compose, Docker Swarm, Nomad, ECS Fargate, Cloud Run. NFR-16 requires on-premise, eliminating cloud-only options. Docker Swarm offers lowest PoC cost but is in maintenance mode with highest migration overhead. K3s is already production-grade; scaling path is multi-cluster federation, not platform replacement. |
|
||||
| **CI/CD** | Jenkins on hospital K3s | Runs inside trusted LAN; connects to cloud-hosted GitLab for source. No external build triggers. |
|
||||
| **Code Hosting** | Self-hosted GitLab CE on cloud VM (NFR-16a exception) | Reliability over hospital-hardware self-hosting; not SaaS. Compensating controls: no PHI in commits (pre-push hook), SSH-only access with cert pinning, daily RDB backup to hospital MinIO, cloud IAM with 2FA + minimum-role, IP whitelist to hospital networks. Exception reviewed at PoC sign-off. |
|
||||
| **Issue Tracking** | Self-hosted Jira on same cloud VM (NFR-16a exception) | Clinical feedback and tickets stay within controlled access boundary. No PHI in tickets per team policy. Same compensating controls as GitLab. |
|
||||
| **Model Serving** | Triton Inference Server | ONNX/TensorRT support; ensemble pipelines; HTTP/gRPC |
|
||||
| **Reverse Proxy** | NGINX + Keepalived | VIP for high availability; SSL termination; instant failover |
|
||||
| **Local LLM** | Browser WebLLM (GemmaE2B) + Cloud MedGemma (Vertex AI) | Browser: Vietnamese + clinical language support, local inference (air-gapped). Cloud: MedGemma for NFR-16a fallback + BERT-triggered arbiter. Triton hosts CV models + EmbeddingGemma only. |
|
||||
| **Vector Search** | pgvector (PostgreSQL HNSW index) | Already deployed with Postgres; zero additional infrastructure; ~15K MOH vectors at ~5-20ms query latency fits NFR-7. Complex SQL filtering for clinical queries. Qdrant deferred to Phase 2. |
|
||||
| **Ontology DB** | ladybugDB embedded | C++ library embedded in FastAPI process; SNOMED-CT/LOINC mappings |
|
||||
|
||||
---
|
||||
|
||||
## 12. Technology Stack Summary
|
||||
|
||||
| Layer | Technology | Purpose |
|
||||
| --- | --- | --- |
|
||||
| Frontend | React, TypeScript, Zustand, Dexie.js, LiteRT, MediaPipe, Transformers.js, OpenRedaction, pii-filter, js-data-anonymizer | PWA with offline support, local inference, and edge guardrail (BERT hallucination/mal-intention detection, Decree 13 PII scrubbing) |
|
||||
| Guardrail | Transformers.js BERT (WebWorker), OpenRedaction, pii-filter, js-data-anonymizer, FastAPI `phi_scrub` middleware, Vertex AI Model Garden safety filters | Edge behavior control without NeMo/GuardrailsAI; prompt-rule + BERT detection; session termination + cloud mitigate; mandatory RAG pre-processing (not optional tool-call); server-side redaction ground-check before NFR-16a egress |
|
||||
| Gateway | NGINX 1.27 + Keepalived | SSL termination, VIP, load balancing |
|
||||
| API | FastAPI, Uvicorn, SQLAlchemy, ReportLab | REST API, ORM, PDF generation |
|
||||
| ML Inference | Triton, ONNX, TensorRT, OpenVINO | Model serving with quantization |
|
||||
| Models | ConvNeXt, DenseNet, ResNet, EfficientNet, Swin-V2, UNet3Plus, DeepLabV3 | Angle, inflammation, segmentation |
|
||||
| Data | PostgreSQL + PostGIS, MinIO, Redis | Relational, object, cache |
|
||||
| Knowledge | pgvector, ladybugDB, GemmaE2B/MedGemma, EmbeddingGemma, BioClinicalBERT | RAG (pgvector HNSW), ontology (ladybugDB), Vietnamese/clinical LLM, 768-dim RAG embeddings, BERT drift/referee |
|
||||
| Observability | Prometheus, Grafana | Metrics and dashboards |
|
||||
| Code Hosting | Self-hosted GitLab CE (cloud VM, NFR-16a exception) | Source control, issue tracking, merge requests, container registry |
|
||||
| CI/CD | Jenkins on hospital K3s | Build, test, deploy pipeline; connects to cloud-hosted GitLab |
|
||||
|
||||
---
|
||||
|
||||
## 13. Cross-References
|
||||
|
||||
| Document | Relevance |
|
||||
| --- | --- |
|
||||
| [SOLUTION_ARCHITECTURE_SPEC.md](../SOLUTION_ARCHITECTURE_SPEC.md) | Main architecture spec, NFR definitions, pattern citations, trade-off analysis |
|
||||
| [Backend Specification](CODEBASE/backend/spec/backend-spec.md) | FastAPI server internal design, API contracts, RAG coordinator |
|
||||
| [Knowledge Stack Specification](CODEBASE/knowledge/spec/knowledge_spec.md) | Qdrant/ladybugDB schema, embedding models, LLM endpoints |
|
||||
| [CI/CD Deployment Pipeline](Design_Material/CI_CD_docs/CI_CD_DEPLOYMENT_PIPELINE.md) | Jenkins pipeline, Docker Compose runtime, offline bundles, rollback |
|
||||
| [DATA_SPEC.md](CODEBASE/data/spec/data_spec.md) | Data contracts, schema definitions |
|
||||
| [CONTEXT_VISION_SCOPE.md](../../PROJ_LEVEL_READING/PLAN/CONTEXT_VISION_SCOPE.md) | Project vision, sprint timelines, user personas |
|
||||
|
||||
---
|
||||
|
||||
## 14. Design Decisions
|
||||
|
||||
| # | Decision | Alternatives Considered | Rationale |
|
||||
| --- | --- | --- | --- |
|
||||
| 1 | Triton for model serving | TorchServe, FastAPI direct | Triton supports ONNX/TensorRT ensembles, dynamic batching, and concurrent model execution |
|
||||
| 2 | K3s over Docker Compose/Swarm/Nomad/ECS/Cloud Run | Docker Compose, Docker Swarm, Nomad, ECS Fargate, Cloud Run | NFR-16 requires on-premise, eliminating cloud-only platforms (ECS, Cloud Run). Docker Swarm is in maintenance mode with highest migration cost to production. K3s is already production-grade; scaling to N hospitals is multi-cluster federation, not platform replacement. |
|
||||
| 3 | MinIO over AWS S3 | Direct filesystem, NFS | S3-compatible API allows future cloud migration; object versioning built-in |
|
||||
| 4 | pgvector over Qdrant/Pinecone | Qdrant, Pinecone, Weaviate | Postgres already deployed; pgvector adds zero infrastructure overhead. At ~15K MOH guideline vectors, HNSW query latency (~5-20ms in shared_buffers) fits within NFR-7 budget. Qdrant advantage appears at millions of vectors or >500 QPS; not needed at PoC scale. Phase 2: introduce Qdrant if corpus exceeds ~100K vectors. |
|
||||
| 5 | ladybugDB embedded | Separate graph DB service | Embedded C++ library reduces latency; no separate process to manage |
|
||||
|
||||
---
|
||||
|
||||
## 15. Open Questions / Future Work (Post Sprint 2)
|
||||
|
||||
| ID | Question | Target Sprint |
|
||||
| --- | --- | --- |
|
||||
| Q1 | How to handle concurrent clinician sessions with shared Triton GPU? | Sprint 3 |
|
||||
| Q2 | Should model weights be versioned in PostgreSQL for A/B testing? | Sprint 4 |
|
||||
| Q3 | How to integrate DICOM C-STORE for automatic PACS ingestion (currently C-MOVE only)? | Sprint 5 |
|
||||
| Q4 | Will GemmaE2B be swapped for MedGemma or Willa when hospital provides GPU cluster? | Sprint 5 |
|
||||
| Q5 | Should Prometheus scrape endpoints be authenticated for NFR-17 audit compliance? | Sprint 3 |
|
||||
Reference in New Issue
Block a user