README.md

metadata

title: MedGemma 1.5 Report Generator
emoji: 🏥
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 5.23.3
app_file: app.py
pinned: false
license: mit

MedGemma 1.5 DICOM Report Generator

A Gradio-based web application that uses Google's MedGemma 1.5 model to automatically generate structured radiology reports from DICOM medical images.

Features

• DICOM Processing: Upload ZIP files containing DICOM images from CT, MR, CR, or DX studies
• Smart Sampling: Configurable slice sampling per series to manage GPU memory
• DICOM Windowing: Auto or manual window/level controls with CT presets (Brain, Lung, Bone, etc.)
• Image Preview: Built-in gallery to visualize sampled slices before inference
• VRAM Estimation: Real-time estimation of GPU memory usage based on settings
• Configurable Generation: Adjustable temperature, top-p, top-k, and max tokens
• Custom Prompts: Editable prompts for tailored report generation

Requirements

• Python 3.10+
• NVIDIA GPU with CUDA support (recommended: 12GB+ VRAM)
• Hugging Face account with access to google/medgemma-1.5-4b-it

Usage

1. Upload a ZIP file containing DICOM images

2. Adjust settings:

   • Max Slices Per Series: Reduce for less VRAM usage
   • Image Size: Smaller images use less VRAM
   • Windowing: Use presets or manual WC/WW for CT images

3. Click "Process & Preview" to see the sampled images and VRAM estimate

4. Click "Generate Report" to create the radiology report

Window Presets

Preset	Window Center	Window Width	Use Case
Brain	40	80	Brain parenchyma
Subdural	75	215	Subdural hematoma
Stroke	32	8	Acute stroke
Lung	-600	1500	Lung parenchyma
Mediastinum	50	350	Mediastinal structures
Bone	400	1800	Bone windows
Abdomen	40	400	Abdominal soft tissue
Liver	60	150	Liver lesions

Tips for Low VRAM

• Use Max Slices Per Series = 5-10 instead of all slices
• Reduce Image Size to 256-384 pixels
• Process one series at a time for very large studies

Disclaimer

This tool is for research and educational purposes only. It is NOT intended for clinical use or medical diagnosis. Always consult qualified healthcare professionals for medical decisions.

License

MIT License

Acknowledgments

• Google MedGemma for the medical vision-language model
• Gradio for the web interface framework
• PyDICOM for DICOM file processing
• Claude Opus (Anthropic) for assistance in creating this demo