README.md

metadata

title: Medical Image Analysis Tool
emoji: 🏥
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 5.49.1
app_file: app.py
pinned: false
license: mit

🏥 Medical Image Analysis Tool

An AI-powered medical image analysis application using advanced detection models and large language models for medical image interpretation.

Features

• Advanced Object Detection: Uses RF-DETR (Real-time Fine-grained Detection Transformer) for precise object detection
• Medical AI Analysis: Integrates MedGemma, a specialized medical vision-language model
• Interactive Interface: Built with Gradio for easy web-based interaction
• Configurable Thresholds: Adjustable confidence thresholds for detection sensitivity
• Model Size Selection: Choose between MedGemma 4B (faster) or 27B (more accurate) models
• GPU Acceleration: Optimized for GPU usage when available with 4-bit quantization
• Automatic Model Downloads: Models download automatically from Hugging Face Hub

Models Used

• RF-DETR Medium: State-of-the-art object detection model
• MedGemma 4B/27B: Medical-specialized vision-language models for analysis and descriptions
  • 4B model: Faster inference, lower memory usage
  • 27B model: Higher accuracy, requires more resources

Usage

1. Upload Image: Click on the image upload area or drag and drop a medical image
2. Adjust Settings:
   • Use the confidence threshold slider to control detection sensitivity
   • Select model size (4B for speed, 27B for accuracy)
3. Analyze: Click "Analyze Image" to run the AI analysis
4. View Results: See the annotated image with detected objects and AI-generated descriptions

Installation & Setup

This application is designed to run on Hugging Face Spaces. The following files are required:

• app.py - Main application file (optimized for Spaces)
• requirements.txt - Python dependencies
• packages.txt - System packages
• README.md - This documentation

Model Loading

🔑 Required: Hugging Face Token (for MedGemma)

MedGemma is a gated model. To use AI-powered text analysis, you must:

1. Go to your Space Settings → Repository secrets
2. Add a new secret:
   • Name: HF_TOKEN
   • Value: Your Hugging Face token (get it from https://huggingface.co/settings/tokens)
3. Important: Accept the model license at https://huggingface.co/google/medgemma-4b-it
4. Save and restart your Space

Without the token: Object detection will still work, but AI text analysis will be disabled.

---

MedGemma Models (Automatic):

• Models download automatically from Hugging Face Hub on first use (with valid token)
• Uses MedGemma 4B for efficient AI-powered analysis
• 4-bit quantization for reduced memory usage

RF-DETR Model (Automatic from HF Model Repo):

• Model automatically downloads from edeler/lorai on Hugging Face
• No manual upload needed - configured in the app
• Cached locally after first download for faster subsequent runs
• Model file: lorai.pth (135MB)

Space Configuration

For optimal performance, configure your Space settings:

• Hardware: GPU (T4 minimum, A100 recommended for 27B models)
• Storage: Enable persistent storage for model caching
• Timeout: 30+ minutes for large model downloads

Technical Details

• Framework: PyTorch + Transformers
• Interface: Gradio
• Computer Vision: OpenCV, PIL, Supervision
• Hardware: Optimized for both CPU and GPU inference

Performance Tips

• Model Selection: Use MedGemma 4B for faster processing or 27B for higher accuracy
• Confidence Thresholds: Higher values reduce false positives but may miss subtle findings
• GPU Acceleration: The application automatically uses GPU acceleration when available
• Memory Optimization: Uses 4-bit quantization to reduce memory usage
• Model Caching: Models are cached after first load for faster subsequent analyses

Limitations

• Requires significant computational resources for optimal performance
• Best suited for medical imaging applications
• Results should be verified by qualified medical professionals

Development

To run locally:

pip install -r requirements.txt
python app.py

Note: For local development, you'll need to:

1. Install the RF-DETR package or ensure it's available
2. Place your rf-detr-medium.pth file in the project directory
3. Models will download automatically on first run

License

This project is for research and educational purposes. Medical applications should be developed and validated according to appropriate regulatory standards.

Support

For issues or questions, please refer to the Hugging Face Space documentation or create an issue in the project repository.

---

title: Medical Image Analysis Tool emoji: 🏥 colorFrom: blue colorTo: green sdk: gradio sdk_version: 5.49.1 app_file: app.py pinned: false license: mit

🏥 Medical Image Analysis Tool

An AI-powered medical image analysis application using advanced detection models and large language models for medical image interpretation.

Features

• Advanced Object Detection: Uses RF-DETR (Real-time Fine-grained Detection Transformer) for precise object detection
• Medical AI Analysis: Integrates MedGemma, a specialized medical vision-language model
• Interactive Interface: Built with Gradio for easy web-based interaction
• Configurable Thresholds: Adjustable confidence thresholds for detection sensitivity
• Model Size Selection: Choose between MedGemma 4B (faster) or 27B (more accurate) models
• GPU Acceleration: Optimized for GPU usage when available with 4-bit quantization
• Automatic Model Downloads: Models download automatically from Hugging Face Hub

Models Used

• RF-DETR Medium: State-of-the-art object detection model
• MedGemma 4B/27B: Medical-specialized vision-language models for analysis and descriptions
  • 4B model: Faster inference, lower memory usage
  • 27B model: Higher accuracy, requires more resources

Usage

1. Upload Image: Click on the image upload area or drag and drop a medical image
2. Adjust Settings:
   • Use the confidence threshold slider to control detection sensitivity
   • Select model size (4B for speed, 27B for accuracy)
3. Analyze: Click "Analyze Image" to run the AI analysis
4. View Results: See the annotated image with detected objects and AI-generated descriptions

Installation & Setup

This application is designed to run on Hugging Face Spaces. The following files are required:

• app.py - Main application file (optimized for Spaces)
• requirements.txt - Python dependencies
• packages.txt - System packages
• README.md - This documentation

Model Loading

RF-DETR Model:

• Upload your trained rf-detr-medium.pth file to the Space
• The application will automatically find and load it

MedGemma Models:

• Models download automatically from Hugging Face Hub on first use
• No manual installation required
• Choose between 4B (faster) or 27B (more accurate) models

Space Configuration

For optimal performance, configure your Space settings:

• Hardware: GPU (T4 minimum, A100 recommended for 27B models)
• Storage: Enable persistent storage for model caching
• Timeout: 30+ minutes for large model downloads

Technical Details

• Framework: PyTorch + Transformers
• Interface: Gradio
• Computer Vision: OpenCV, PIL, Supervision
• Hardware: Optimized for both CPU and GPU inference

Performance Tips

• Model Selection: Use MedGemma 4B for faster processing or 27B for higher accuracy
• Confidence Thresholds: Higher values reduce false positives but may miss subtle findings
• GPU Acceleration: The application automatically uses GPU acceleration when available
• Memory Optimization: Uses 4-bit quantization to reduce memory usage
• Model Caching: Models are cached after first load for faster subsequent analyses

Limitations

• Requires significant computational resources for optimal performance
• Best suited for medical imaging applications
• Results should be verified by qualified medical professionals

Development

To run locally:

pip install -r requirements.txt
python app.py

Note: For local development, you'll need to:

1. Install the RF-DETR package or ensure it's available
2. Place your rf-detr-medium.pth file in the project directory
3. Models will download automatically on first run

License

This project is for research and educational purposes. Medical applications should be developed and validated according to appropriate regulatory standards.

Support

For issues or questions, please refer to the Hugging Face Space documentation or create an issue in the project repository.