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README.md
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---
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title: MediSync - Multi-Modal Medical Analysis System
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emoji: π©Ί
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colorFrom: blue
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colorTo: purple
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sdk: gradio
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sdk_version: 5.20.1
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app_file: app.py
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pinned: false
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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# MediSync: Multi-Modal Medical Analysis System
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MediSync is an AI-powered healthcare solution that combines X-ray image analysis with patient report text processing to provide comprehensive medical insights.
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Introduction
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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:
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Analyze chest X-ray images to detect abnormalities
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Extract key clinical information from medical reports
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Fuse insights from both modalities for enhanced diagnosis support
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Provide comprehensive visualization of analysis results
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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.
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System Architecture
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MediSync follows a modular architecture with three main components:
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Image Analysis Module: Processes X-ray images using pre-trained vision models
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Text Analysis Module: Analyzes medical reports using NLP models
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Multimodal Fusion Module: Combines insights from both modalities
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The system uses the following high-level workflow:
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βββββββββββββββββββ
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β X-ray Image β
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ββββββββββ¬βββββββββ
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β
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βΌ
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βββββββββββββββββββ βββββββββββββββββββ βββββββββββββββββββ
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β Preprocessing βββββΆβ Image Analysis βββββΆβ β
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βββββββββββββββββββ βββββββββββββββββββ β β
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β Multimodal β
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βββββββββββββββββββ βββββββββββββββββββ β Fusion βββββΆ Results
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β Medical Report βββββΆβ Text Analysis βββββΆβ β
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βββββββββββββββββββ βββββββββββββββββββ β β
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βββββββββββββββββββ
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## Features
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- **X-ray Image Analysis**: Detects abnormalities in chest X-rays using pre-trained vision models from Hugging Face.
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- **Medical Report Processing**: Extracts key information from patient reports using NLP models.
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- **Multi-modal Integration**: Combines insights from both image and text data for more accurate diagnosis suggestions.
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- **User-friendly Interface**: Simple web interface for uploading images and reports.
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## Project Structure
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```
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mediSync/
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βββ app.py # Main application with Gradio interface
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βββ models/
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β βββ image_analyzer.py # X-ray image analysis module
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β βββ text_analyzer.py # Medical report text analysis module
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β βββ multimodal_fusion.py # Fusion of image and text insights
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βββ utils/
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β βββ preprocessing.py # Data preprocessing utilities
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β βββ visualization.py # Result visualization utilities
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βββ data/
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β βββ sample/ # Sample data for testing
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βββ tests/ # Unit tests
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```
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## Setup Instructions
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1. Clone this repository:
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```bash
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git clone [repository-url]
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cd MediSync
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```
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2. Install dependencies:
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```bash
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pip install -r requirements.txt
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```
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3. Run the application:
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```bash
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python app.py
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```
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4. Access the web interface at `http://localhost:7860`
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## Models Used
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- **X-ray Analysis**: facebook/deit-base-patch16-224-medical-cxr
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- **Medical Text Analysis**: medicalai/ClinicalBERT
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- **Additional Support Models**: Medical question answering and entity recognition models
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## Use Cases
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- Preliminary screening of chest X-rays
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- Cross-validation of radiologist reports
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- Educational tool for medical students
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- Research tool for studying correlation between visual findings and written reports
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## Note
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This system is designed as a support tool and should not replace professional medical diagnosis. Always consult with healthcare professionals for medical decisions.
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