metadata

title: MediSync - Multi-Modal Medical Analysis System
emoji: 🩺
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.20.1
app_file: app.py
pinned: false

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

MediSync: Multi-Modal Medical Analysis System

MediSync is an AI-powered healthcare solution that combines X-ray image analysis with patient report text processing to provide comprehensive medical insights.

Introduction MediSync is a multi-modal AI system that combines X-ray image analysis with medical report text processing to provide comprehensive medical insights. By leveraging state-of-the-art deep learning models for both vision and language understanding, MediSync can:

Analyze chest X-ray images to detect abnormalities Extract key clinical information from medical reports Fuse insights from both modalities for enhanced diagnosis support Provide comprehensive visualization of analysis results This AI system demonstrates the power of multi-modal fusion in the healthcare domain, where integrating information from multiple sources can lead to more robust and accurate analyses.

System Architecture MediSync follows a modular architecture with three main components:

Image Analysis Module: Processes X-ray images using pre-trained vision models Text Analysis Module: Analyzes medical reports using NLP models Multimodal Fusion Module: Combines insights from both modalities The system uses the following high-level workflow:

                  ┌─────────────────┐
                  │    X-ray Image  │
                  └────────┬────────┘
                           │
                           ▼

┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │ Preprocessing │───▶│ Image Analysis │───▶│ │ └─────────────────┘ └─────────────────┘ │ │ │ Multimodal │ ┌─────────────────┐ ┌─────────────────┐ │ Fusion │───▶ Results │ Medical Report │───▶│ Text Analysis │───▶│ │ └─────────────────┘ └─────────────────┘ │ │ └─────────────────┘

Features

X-ray Image Analysis: Detects abnormalities in chest X-rays using pre-trained vision models from Hugging Face.
Medical Report Processing: Extracts key information from patient reports using NLP models.
Multi-modal Integration: Combines insights from both image and text data for more accurate diagnosis suggestions.
User-friendly Interface: Simple web interface for uploading images and reports.

Project Structure

mediSync/
├── app.py                    # Main application with Gradio interface
├── models/
│   ├── image_analyzer.py     # X-ray image analysis module
│   ├── text_analyzer.py      # Medical report text analysis module
│   └── multimodal_fusion.py  # Fusion of image and text insights
├── utils/
│   ├── preprocessing.py      # Data preprocessing utilities
│   └── visualization.py      # Result visualization utilities
├── data/
│   └── sample/               # Sample data for testing
└── tests/                    # Unit tests

Setup Instructions

Clone this repository:

git clone [repository-url]
cd MediSync

Install dependencies:

pip install -r requirements.txt

Run the application:

python app.py

Access the web interface at http://localhost:7860

Models Used

X-ray Analysis: facebook/deit-base-patch16-224-medical-cxr
Medical Text Analysis: medicalai/ClinicalBERT
Additional Support Models: Medical question answering and entity recognition models

Use Cases

Preliminary screening of chest X-rays
Cross-validation of radiologist reports
Educational tool for medical students
Research tool for studying correlation between visual findings and written reports

Note

This system is designed as a support tool and should not replace professional medical diagnosis. Always consult with healthcare professionals for medical decisions.