# Expose Pixel-Level Activation Logic

Actor: UP5
DateAdd: June 7, 2026 10:21 PM
Engineer: Đạt Trần Tiến (Daves Tran)
Functional Requirement Engineer DB: CHUẨN ĐOÁN Phân loại Mức độ Viêm Khớp gối (https://app.notion.com/p/CHU-N-O-N-Ph-n-lo-i-M-c-Vi-m-Kh-p-g-i-375f910aea75800199d4feb8b07f9145?pvs=21)
Goal: Reveal fine-grained layer weights and activation responses when the human specialist challenges an automated grade prediction
Interaction: User-to-System
Stimulus: Clinician manually alters or rejects the ML-proposed classification score in the review pane
SysResponse: Interactive visual mapping showing the exact high-frequency noise regions driving model prediction errors
Title [Verb + Noun]:  Expose Pixel-Level Activation Logic
UC-ID: UC-25637
VerboseForm: The use case ' Expose Pixel-Level Activation Logic' defines a User-to-System interaction where the UP5 aims to Reveal fine-grained layer weights and activation responses when the human specialist challenges an automated grade prediction. This workflow is triggered when Clinician manually alters or rejects the ML-proposed classification score in the review pane, causing the system to respond by providing Interactive visual mapping showing the exact high-frequency noise regions driving model prediction errors.

```markdown

```markdown
# Use Case Deep-Dive: Expose Pixel-Level Activation Logic

## 1. Structural Preconditions & Postconditions
* **Preconditions:**
  * Active session is inside the `UC_Review` interface phase.
  * Specialist chooses an option that breaks clean model agreement paths.
* **Postconditions (Success State):**
  * Internal neural layer weight vectors are visually mapped onto the primary medical viewport.
  * Core manual artifact isolation tool sets become active on the canvas layout.

---

## 2. Interaction Scenarios (Step-by-Step Flow)

### Main Success Scenario (Happy Path)
1. **Diagnostic Radiologist** changes the system-suggested grade classification dropdown setting.
2. **System** captures the modification step and branches away from the standard review pathway to reveal underlying model mechanics.
3. **System** transforms image layers to display fine-grained activation weights, revealing exactly which pixel clusters (e.g., acoustic shadowing regions or bone interfaces) skewed the model's calculation.
4. **System** includes `UC_Q3_Isolate` to let the specialist manually clean up the noise zones.

---

## 3. PlantUML Visual Model
```plantuml
@startuml
left to right direction
skin rose

actor "Diagnostic Radiologist" as Rad

rectangle "VKIST MSK Workspace (FR-25)" {
    usecase "Review Suggested Synovitis Grade (0-3)" as UC_Review
    usecase "Expose Pixel-Level Activation Logic" as UC_Core
    usecase "Isolate Visual Noise/Artifacts" as UC_Sub
}

Rad --> UC_Review
UC_Review <.. UC_Core : <<extend>> (If clinician contests AI score)
UC_Core ..> UC_Sub : <<include>>
@enduml

```
```

![image.png](Expose%20Pixel-Level%20Activation%20Logic/image.png)