<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://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://docs.monai.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://docs.monai.io/en/latest/highlights.html) and [What's New](https://docs.monai.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://docs.monai.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 [docs.monai.io](https://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](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://docs.monai.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://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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"description": "<p align=\"center\">\n <img src=\"https://raw.githubusercontent.com/Project-MONAI/MONAI/dev/docs/images/MONAI-logo-color.png\" width=\"50%\" alt='project-monai'>\n</p>\n\n**M**edical **O**pen **N**etwork for **AI**\n\n\n[](https://opensource.org/licenses/Apache-2.0)\n[](https://badge.fury.io/py/monai)\n[](https://hub.docker.com/r/projectmonai/monai)\n[](https://anaconda.org/conda-forge/monai)\n\n[](https://github.com/Project-MONAI/MONAI/actions/workflows/pythonapp.yml)\n[](https://github.com/Project-MONAI/MONAI/actions?query=branch%3Adev)\n[](https://docs.monai.io/en/latest/)\n[](https://codecov.io/gh/Project-MONAI/MONAI)\n[](https://piptrends.com/package/monai)\n\nMONAI 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/).\nIts ambitions are as follows:\n\n- Developing a community of academic, industrial and clinical researchers collaborating on a common foundation;\n- Creating state-of-the-art, end-to-end training workflows for healthcare imaging;\n- Providing researchers with the optimized and standardized way to create and evaluate deep learning models.\n\n## Features\n\n> _Please see [the technical highlights](https://docs.monai.io/en/latest/highlights.html) and [What's New](https://docs.monai.io/en/latest/whatsnew.html) of the milestone releases._\n\n- flexible pre-processing for multi-dimensional medical imaging data;\n- compositional & portable APIs for ease of integration in existing workflows;\n- domain-specific implementations for networks, losses, evaluation metrics and more;\n- customizable design for varying user expertise;\n- multi-GPU multi-node data parallelism support.\n\n## Requirements\n\nMONAI works with the [currently supported versions of Python](https://devguide.python.org/versions), and depends directly on NumPy and PyTorch with many optional dependencies.\n\n* 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.\n* 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.\n* 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. 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The citation can be exported from: <https://arxiv.org/abs/2211.02701>.\n\n## Model Zoo\n\n[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.\nUtilizing [the MONAI Bundle format](https://docs.monai.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.\n\n## Contributing\n\nFor guidance on making a contribution to MONAI, see the [contributing guidelines](https://github.com/Project-MONAI/MONAI/blob/dev/CONTRIBUTING.md).\n\n## Community\n\nJoin 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).\n\nAsk and answer questions over on [MONAI's GitHub Discussions tab](https://github.com/Project-MONAI/MONAI/discussions).\n\n## Links\n\n- Website: <https://monai.io/>\n- API documentation (milestone): <https://docs.monai.io/>\n- API documentation (latest dev): <https://docs.monai.io/en/latest/>\n- Code: <https://github.com/Project-MONAI/MONAI>\n- Project tracker: <https://github.com/Project-MONAI/MONAI/projects>\n- Issue tracker: <https://github.com/Project-MONAI/MONAI/issues>\n- Wiki: <https://github.com/Project-MONAI/MONAI/wiki>\n- Test status: <https://github.com/Project-MONAI/MONAI/actions>\n- PyPI package: <https://pypi.org/project/monai/>\n- conda-forge: <https://anaconda.org/conda-forge/monai>\n- Weekly previews: <https://pypi.org/project/monai-weekly/>\n- Docker Hub: <https://hub.docker.com/r/projectmonai/monai>\n",
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