Medical Open Network for AI
- developing a community of academic, industrial and clinical researchers collaborating on a common foundation;
- creating state-of-the-art, end-to-end training workflows for healthcare imaging;
- providing researchers with the optimized and standardized way to create and evaluate deep learning models.
- flexible pre-processing for multi-dimensional medical imaging data;
- compositional & portable APIs for ease of integration in existing workflows;
- domain-specific implementations for networks, losses, evaluation metrics and more;
- customizable design for varying user expertise;
- multi-GPU data parallelism support.
To install the current release, you can simply run:
pip install monai
For other installation methods (using the default GitHub branch, using Docker, etc.), please refer to the installation guide.
Examples and notebook tutorials are located at Project-MONAI/tutorials.
Technical documentation is available at docs.monai.io.
For guidance on making a contribution to MONAI, see the contributing guidelines.
Ask and answer questions over on MONAI's GitHub Discussions tab.
- Website: https://monai.io/
- API documentation: https://docs.monai.io
- Code: https://github.com/Project-MONAI/MONAI
- Project tracker: https://github.com/Project-MONAI/MONAI/projects
- Issue tracker: https://github.com/Project-MONAI/MONAI/issues
- Wiki: https://github.com/Project-MONAI/MONAI/wiki
- Test status: https://github.com/Project-MONAI/MONAI/actions
- PyPI package: https://pypi.org/project/monai/
- Weekly previews: https://pypi.org/project/monai-weekly/
- Docker Hub: https://hub.docker.com/r/projectmonai/monai