README.md

metadata

title: DICOM Interpreter with MONAI
emoji: 🩻
colorFrom: blue
colorTo: indigo
sdk: gradio
sdk_version: 5.29.0
app_file: app.py
pinned: false

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

DICOM Interpreter with MONAI

This application demonstrates how to use MONAI (Medical Open Network for AI) to analyze DICOM medical images.

Features

• Upload and view DICOM medical images
• AI-assisted interpretation using MONAI
• Visualization of AI attention maps
• Display of important DICOM metadata

How to Use

1. Click the "Upload DICOM File" button to select a DICOM (.dcm) file from your computer
2. Click "Analyze DICOM" to process the image
3. View the results in the display panels

Technical Details

This application uses:

• MONAI: A PyTorch-based framework for deep learning in healthcare imaging
• PyDICOM: A Python library for working with DICOM files
• Gradio: A Python library for creating customizable UI components for ML models
• PyTorch: The deep learning framework that powers the models

Disclaimer

This is a demonstration application and should not be used for clinical diagnosis. The models used here are generic and not specifically trained or validated for medical diagnosis. In a real clinical setting, models should be properly trained, validated, and approved by appropriate regulatory bodies.

About

This application was created to demonstrate how AI can be integrated into medical imaging workflows. The approach demonstrated here can be extended to more sophisticated models trained on specific medical datasets.