Project-MONAI/MONAI

Medical Open Network for AI

MONAI is a PyTorch-based, open-source framework for deep learning in healthcare imaging, part of the PyTorch Ecosystem. Its ambitions are as follows:

• 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.

Please see the technical highlights and What's New of the milestone releases.

• 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 multi-node data parallelism support.

MONAI works with the currently supported versions of Python, and depends directly on NumPy and PyTorch with many optional dependencies.

• Major releases of MONAI will have dependency versions stated for them. The current state of the dev branch in this repository is the unreleased development version of MONAI which typically will support current versions of dependencies and include updates and bug fixes to do so.
• PyTorch support covers the current version plus three previous minor versions. If compatibility issues with a PyTorch version and other dependencies arise, support for a version may be delayed until a major release.
• Our support policy for other dependencies adheres for the most part to SPEC0, where dependency versions are supported where possible for up to two years. Discovered vulnerabilities or defects may require certain versions to be explicitly not supported.
• See the pyproject.toml file for dependency version information.

To install the current release, you can simply run:

pip install monai

Please refer to the installation guide for other installation options.

MedNIST demo and MONAI for PyTorch Users are available on Colab.

Examples and notebook tutorials are located at Project-MONAI/tutorials.

Technical documentation is available at monai.readthedocs.io.

The MONAI Docker image is available from Dockerhub, tagged as latest for the latest state of dev or with a release version. A slimmed down image can also be built locally using Dockerfile.slim, see that file for instructions.

To get started with the latest MONAI, use docker run -ti --rm --gpus all projectmonai/monai:latest /bin/bash.

If you have used MONAI in your research, please cite us! The citation can be exported from: https://arxiv.org/abs/2211.02701.

The MONAI Model Zoo is a place for researchers and data scientists to share the latest and great models from the community. Utilizing the MONAI Bundle format makes it easy to get started building workflows with MONAI.

For guidance on making a contribution to MONAI, see the contributing guidelines.

Join the conversation on Twitter/X @ProjectMONAI, LinkedIn, or join our Slack channel.

Ask and answer questions over on MONAI's GitHub Discussions tab.

• Website: https://project-monai.github.io/
• API documentation (milestone): https://monai.readthedocs.io/
• API documentation (latest dev): 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
• PyPI package: https://pypi.org/project/monai/
• conda-forge: https://anaconda.org/conda-forge/monai
• Weekly previews: https://pypi.org/project/monai-weekly/
• Docker Hub: https://hub.docker.com/r/projectmonai/monai