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.

Features

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.

Requirements

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 requirements*.txt files for dependency version information.

Installation

To install the current release, you can simply run:

pip install monai

Please refer to the installation guide for other installation options.

Getting Started

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 docs.monai.io.

Citation

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

Model Zoo

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.

Contributing

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

Community

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

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

Links

Website: https://monai.io/
API documentation (milestone): https://docs.monai.io/
API documentation (latest dev): https://docs.monai.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

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