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MONAI
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AI Toolkit for Healthcare Imaging
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MONAI
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<p align="center"> <img src="https://raw.githubusercontent.com/Project-MONAI/MONAI/dev/docs/images/MONAI-logo-color.png" width="50%" alt='project-monai'> </p> **M**edical **O**pen **N**etwork for **AI**  [](https://opensource.org/licenses/Apache-2.0) [](https://arxiv.org/abs/2211.02701) [](https://badge.fury.io/py/monai) [](https://hub.docker.com/r/projectmonai/monai) [](https://anaconda.org/conda-forge/monai) [](https://github.com/Project-MONAI/MONAI/actions/workflows/pythonapp.yml) [](https://github.com/Project-MONAI/MONAI/actions?query=branch%3Adev) [](https://monai.readthedocs.io/en/latest/) [](https://codecov.io/gh/Project-MONAI/MONAI) [](https://piptrends.com/package/monai) 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>
