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| 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) |