Med-Physics

A comprehensive repository combining Medical Physics with Data Science and AI Engineering, focused on medical data analysis, state-of-the-art healthcare applications, computer vision, and model explainability.

🎯 Project Overview

Med-Physics is a research-oriented repository that bridges the gap between Medical Physics and Advanced Data Science techniques. This is an evolving project that aims to:

• Analyze and process various types of medical data (EEG, neuroimaging, cardiometabolic biomarkers, etc.)
• Test and implement state-of-the-art AI models and tools for healthcare
• Experiment with transfer learning and fine-tuning of pre-trained medical AI models
• Develop computer vision solutions for medical imaging
• Explore explainability techniques for healthcare AI systems
• Create reproducible research workflows
• Document best practices in medical data science

🤖 AI Engineering Focus

The repository actively explores and implements cutting-edge AI solutions in healthcare:

Pre-trained Models & Tools

• Integration and testing of SOTA healthcare models from HuggingFace
• Experimentation with leading medical imaging models
• Fine-tuning pre-trained models for specific medical tasks
• Benchmarking different model architectures

Healthcare AI Applications

• Medical image segmentation and classification
• Disease prediction and progression modeling
• Biomarker analysis and patient stratification
• EEG signal processing and analysis

Note: The list of models and applications will expand as new tools are tested and integrated. Each implementation will be documented in dedicated notebooks with performance analyses and use cases.

🗂️ Repository Structure

Med-Physics/
├── data/                      
│   ├── raw/                   
│   ├── processed/             
│   └── external/             
├── src/                    
│   ├── data_processing/   
│   ├── models/               
│   │   ├── traditional/      
│   │   └── deep_learning/ 
│   ├── visualization/        
│   └── explainability/       
├── notebooks/                 
│   ├── exploratory/          
│   ├── model_development/    
│   └── results_analysis/    
├── docs/                     
│   ├── data_documentation/   
│   ├── model_documentation/ 
│   └── research_papers/       
├── tests/                    
├── configs/                   
├── mlflow/                   
│   ├── mlruns/               
│   └── artifacts/             
└── results/               
    ├── figures/            
    ├── models/               
    └── reports/              

📊 Data Sources

The repository works with various types of medical data:

• EEG Data: Brain electrical activity measurements
• Neuroimaging: MRI, fMRI, CT scans
• Cardiometabolic Biomarkers: Blood markers, vital signs
• Neurodegenerative Disease Markers: Alzheimer's, Parkinson's indicators

Primary data sources include:

• PhysioNet
• ADNI (Alzheimer's Disease Neuroimaging Initiative)
• OASIS (Open Access Series of Imaging Studies)

🛠️ Technologies

Note: This is a growing list that will be updated as new tools and frameworks are integrated into the project.

Current Stack:

• Programming: Python 3.8+
• Data Processing:
  • Pandas, NumPy
  • SciPy
  • Nibabel (for neuroimaging)
• Machine Learning & AI:
  • PyTorch
  • Transformers (Hugging Face)
  • TensorFlow/Keras
• Healthcare AI Tools:
  • [To be expanded with tested tools]
  • [Will include successful implementations]
• Visualization:
  • Matplotlib
  • Seaborn
  • Plotly
  • PyGWalker and Streamlit
• Experiment Tracking:
  • MLflow
• Development Tools:
  • Git
  • Docker
  • pytest

📈 Workflow & Development Process

This is an iterative development process that includes:

1. Tool & Model Exploration:

   • Research current SOTA models and tools
   • Initial testing in isolated notebooks
   • Performance evaluation and documentation
   • Integration decision based on results

2. Data Processing:

   • Data collection and validation
   • Preprocessing pipeline development
   • Feature engineering
   • Quality assurance protocols

3. Model Development & Testing:

   • Experiment tracking with MLflow
   • Model training and validation
   • Fine-tuning experiments
   • Performance evaluation
   • Integration with existing tools

4. Analysis and Documentation:

   • Result visualization
   • Model explainability
   • Performance metrics
   • Clinical relevance assessment
   • Documentation of learnings and best practices

Each component of the workflow will be expanded and refined as the project evolves. Successful implementations will be documented and integrated into the main codebase.

🚀 Getting Started

1. Clone the repository:

git clone https://github.com/yourusername/Med-Physics.git
cd Med-Physics

2. Set up the environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

3. Initialize MLflow:

mlflow ui

4. Start exploring the notebooks in the notebooks/ directory

🚧 Project Status

This repository is actively under development. New models, tools, and applications are being tested and integrated regularly. Check the project boards and issues for current focus areas and upcoming features.

📝 License

This project is licensed under the Apache License - see the LICENSE file for details.