- 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.
The codebase is currently under active development.
- 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:
pip install monai
To install from the source code repository:
pip install git+https://github.com/Project-MONAI/MONAI#egg=MONAI
Alternatively, pre-built Docker image is available via DockerHub:
# with docker v19.03+ docker run --gpus all --rm -ti --ipc=host projectmonai/monai:latest
Tutorials & examples are located at monai/examples.
Technical documentation is available via Read the Docs.
For guidance on making a contribution to MONAI, see the contributing guidelines.
- Website: https://monai.io/
- API documentation: https://monai.readthedocs.io/en/latest/
- 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