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AI Toolkit for Healthcare Imaging

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**M**edical **O**pen **N**etwork for **AI**

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MONAI is a [PyTorch](https://pytorch.org/)-based, [open-source](https://github.com/Project-MONAI/MONAI/blob/dev/LICENSE) framework for deep learning in healthcare imaging, part of the [PyTorch Ecosystem](https://pytorch.org/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](https://monai.readthedocs.io/en/latest/highlights.html) and [What's New](https://monai.readthedocs.io/en/latest/whatsnew.html) 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](https://devguide.python.org/versions), 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](https://github.com/pytorch/pytorch/releases) 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](https://scientific-python.org/specs/spec-0000), 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](https://pypi.org/project/monai/), you can simply run:

```bash
pip install monai
```

Please refer to [the installation guide](https://monai.readthedocs.io/en/latest/installation.html) for other installation options.

## Getting Started

[MedNIST demo](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/main/2d_classification/mednist_tutorial.ipynb) and [MONAI for PyTorch Users](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/main/modules/developer_guide.ipynb) are available on Colab.

Examples and notebook tutorials are located at [Project-MONAI/tutorials](https://github.com/Project-MONAI/tutorials).

Technical documentation is available at [monai.readthedocs.io](https://monai.readthedocs.io).

## Docker

The MONAI Docker image is available from [Dockerhub](https://hub.docker.com/r/projectmonai/monai),
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`.

## 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](https://github.com/Project-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](https://monai.readthedocs.io/en/latest/bundle_intro.html) makes it easy to [get started](https://github.com/Project-MONAI/tutorials/tree/main/model_zoo) building workflows with MONAI.

## Contributing

For guidance on making a contribution to MONAI, see the [contributing guidelines](https://github.com/Project-MONAI/MONAI/blob/dev/CONTRIBUTING.md).

## Community

Join the conversation on Twitter/X [@ProjectMONAI](https://twitter.com/ProjectMONAI), [LinkedIn](https://www.linkedin.com/company/projectmonai), or join our [Slack channel](https://forms.gle/QTxJq3hFictp31UM9).

Ask and answer questions over on [MONAI's GitHub Discussions tab](https://github.com/Project-MONAI/MONAI/discussions).

## Links

- 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>

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