Medic-AI is a Keras based library designed for medical image analysis using machine learning techniques. Its core strengths include:

• Backend Agnostic: Compatible with tensorflow, torch, and jax.
• User-Friendly API: High-level interface for transformations and model creation.
• Scalable Execution: Supports training and inference on single/multi-GPU and TPU-VM setups.
• Essential Components: Includes standard metrics and losses, such as Dice.
• Optimized 3D Inference: Offers an efficient sliding-window method and callback for volumetric data

📋 Table of Contents

1. Installation
2. Guides
3. Documentation
4. Acknowledgements
5. Citation

🛠 Installation

PyPI version:

!pip install medicai

Installing from source GitHub:

!pip install git+https://github.com/innat/medic-ai.git

💡 Guides

Segmentation: Available guides for 3D segmentation task.

Task	GitHub	Kaggle	View
Covid-19
BTCV	coming soon	coming soon	n/a
BraTS	coming soon	coming soon	n/a
Spleen	coming soon	coming soon	n/a

Classification: Available guides for 3D classification task.

Task (Classification)	GitHub	Kaggle
Covid-19

📚 Documentation

To learn more about model, transformation, and training, please visit official documentation: medicai/docs

🤝 Contributing

Please refer to the current roadmap for an overview of the project. Feel free to explore anything that interests you. If you have suggestions or ideas, I’d appreciate it if you could open a GitHub issue so we can discuss them further.

1. Install medicai from soruce:

!git clone https://github.com/innat/medic-ai
%cd medic-ai
!pip install keras -qU
!pip install -e .
%cd ..

Add your contribution and implement relevant test code.

2. Run test code as:

python -m pytest test/

# or, only one your new_method
python -m pytest -k new_method

🙏 Acknowledgements

This project is greatly inspired by MONAI.

📝 Citation

If you use medicai in your research or educational purposes, please cite it using the metadata from our CITATION.cff file.