VaradBhagwat5/structure-aware-medical-diffusion
Domain:
healthcareRecord type:
model
Creator:
Var
Host:
A structure-aware conditional diffusion framework for enhancing degraded medical images in low-resource radiology settings, with explicit structural preservation constraints and comparison against GAN-based methods.
# Teammate C — Backend + Frontend
## Project structure
```
api/
main.py ← FastAPI app, /enhance endpoint
infer.py ← ESRGAN / Pix2Pix inference wrapper
requirements.txt
frontend/src/app/
services/enhancement.service.ts ← HTTP client
components/enhancer/
enhancer.component.ts ← logic
enhancer.component.html ← UI: upload · slider · heatmap
enhancer.component.scss ← dark-theme styles
app.module.ts
```
---
## Backend setup
```bash
cd api
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
uvicorn main:app --reload --port 8000
```
Health check → localhost
### Connecting real model checkpoints
Open `infer.py` and replace the `TODO` lines with your actual checkpoint paths:
```python
# ESRGAN
m.load_state_dict(torch.load("checkpoints/esrgan_x4.pth", map_location=self.device))
# Pix2Pix
m.load_state_dict(torch.load("checkpoints/pix2pix.pth", map_location=self.device))
```
---
## Frontend setup
```bash
cd frontend
npm install
ng serve # dev server → localhost
```
### UI features
| Feature | Description |
|---|---|
| Drag-and-drop upload | Drop zone or click-to-browse |
| Model selector | ESRGAN (super-res) · Pix2Pix (translation) |
| Scale buttons | ×2 / ×4 / ×8 (ESRGAN) |
| Strength slider | Blend 0–100% enhancement |
| Before/after comparison | Draggable divider slider |
| Attention heatmap | Toggle canvas overlay |
| Download | Save enhanced PNG |
---
## API reference
### `POST /enhance`
| Field | Type | Default | Description |
|---|---|---|---|
| `file` | image/* | — | Input image (max 20 MB) |
| `model` | string | `esrgan` | `esrgan` or `pix2pix` |
| `scale` | int | `4` | Upscale factor (ESRGAN only) |
| `strength` | float | `1.0` | Enhancement blend (0.0–1.0) |
**Response**
```json
{
"enhanced_image": " ",
"heatmap": " ",
"model": "esrgan",
"scale": 4,
"inference_time_s": 0.342,
"original_size": [512, 512],
"enhanced …
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