---
license: mit
title: Radiology Report Generation System
sdk: docker
emoji: 🏢
colorFrom: blue
colorTo: green
---
# Radiology AI Report Generation System v2.0

AI-powered radiology report generation using BLIP-2 vision + Phi-2 LLM fine-tuned on real radiology reports.

## What it does
- Upload any radiology image (X-ray, CT, MRI, Ultrasound, PET, Mammography, etc.)
- BLIP-2 reads the actual image and describes what it sees
- Phi-2 (fine-tuned on 227,835 real radiology reports) generates a structured report
- Outputs: FINDINGS, IMPRESSION, RECOMMENDATIONS + UMLS/ICD-10/SNOMED CT codes

## Requirements
- Windows 10/11
- Python 3.10
- NVIDIA GPU with 8GB+ VRAM (RTX 4060 or better)
- CUDA 12.8
- 20GB free disk space (for models)

---

## Quick Start

### Step 1 — Setup (run once)
```
Double-click: setup.bat
```
This creates venv, installs all dependencies, verifies GPU.

### Step 2 — Fine-tune (run once, ~35 minutes)
```
Double-click: run_training.bat
```
Downloads unsloth/Radiology_mini (227,835 real reports) and fine-tunes Phi-2.

### Step 3 — Start the system
Terminal 1:
```
run_backend.bat
```
Terminal 2:
```
run_frontend.bat
```

### Step 4 — Use the system
Open browser: http://localhost:7860

---

## Manual commands (if .bat files don't work)

```bash
cd D:\radiology_ai_system
venv\Scripts\activate

# Backend
set USE_4BIT=true
set ENABLE_VISION_CAPTION=true
uvicorn backend.main:app --reload --host 0.0.0.0 --port 8000

# Frontend (new terminal)
venv\Scripts\activate
python frontend/ui.py

# Fine-tuning (Git Bash)
python training/train_unsloth.py --max_steps 300
```

---

## Project Structure
```
radiology_ai_system/
├── backend/
│   ├── main.py              # FastAPI endpoints
│   ├── model_loader.py      # BLIP-2 + Phi-2 loading
│   ├── preprocessing.py     # Image preprocessing (9 modalities)
│   ├── report_generator.py  # Report generation + parsing
│   ├── validation_module.py # Report quality scoring
│   └── medical_coding.py    # UMLS/ICD-10/SNOMED CT
├── frontend/
│   └── ui.py                # Gradio UI
├── training/
│   ├── train_unsloth.py     # Fine-tuning on Radiology_mini
│   ├── prepare_dataset.py   # Dataset preparation
│   └── evaluate_model.py    # Model evaluation
├── models/
│   ├── slm_model/           # Phi-2 cached here
│   │   └── lora_adapter/    # Fine-tuned LoRA adapter saved here
│   └── vision_encoder/      # BLIP-2 cached here
├── setup.bat                # One-time setup
├── run_backend.bat          # Start backend
├── run_frontend.bat         # Start frontend
├── run_training.bat         # Fine-tune model
└── requirements.txt
```

---

## VRAM Usage
| Component     | VRAM   |
|---------------|--------|
| Phi-2 (4-bit) | ~2.0GB |
| BLIP-2        | ~3.5GB |
| Total         | ~5.5GB |
| Available     | 8.6GB  ✅ |

---

## API Endpoints
- POST /api/generate-report  — Generate report from image
- GET  /api/report/{id}      — Get stored report
- GET  /api/export/pdf/{id}  — Download PDF
- GET  /api/export/json/{id} — Download JSON
- GET  /api/health           — System health
- GET  /docs                 — Swagger UI (http://localhost:8000/docs)