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https://github.com/silvano315/med-physics

This would be a repository about medical physics. It will based on 4 paths: medical data to analyse, SOTA programs for medical purposes, computer vision and eXplainability.
https://github.com/silvano315/med-physics

computer-vision data-analysis data-science explainable-ai medical-imaging medical-physics medical-tool

Last synced: 6 days ago
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This would be a repository about medical physics. It will based on 4 paths: medical data to analyse, SOTA programs for medical purposes, computer vision and eXplainability.

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README 

        
# 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:

```bash

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

cd Med-Physics

```



2. Set up the environment:

```bash

python -m venv venv

source venv/bin/activate  # On Windows: venv\Scripts\activate

pip install -r requirements.txt

```



3. Initialize MLflow:

```bash

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.