<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.
## 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/drive/1wy8XUSnNWlhDNazFdvGBHLfdkGvOHBKe) and [MONAI for PyTorch Users](https://colab.research.google.com/drive/1boqy7ENpKrqaJoxFlbHIBnIODAs1Ih1T) 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) 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- 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\n## Features\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\n## Installation\n\nTo install [the current release](https://pypi.org/project/monai/), you can simply run:\n\n```bash\npip install monai\n```\n\nPlease refer to [the installation guide](https://docs.monai.io/en/latest/installation.html) for other installation options.\n\n## Getting Started\n\n[MedNIST demo](https://colab.research.google.com/drive/1wy8XUSnNWlhDNazFdvGBHLfdkGvOHBKe) and [MONAI for PyTorch Users](https://colab.research.google.com/drive/1boqy7ENpKrqaJoxFlbHIBnIODAs1Ih1T) are available on Colab.\n\nExamples and notebook tutorials are located at [Project-MONAI/tutorials](https://github.com/Project-MONAI/tutorials).\n\nTechnical documentation is available at [docs.monai.io](https://docs.monai.io).\n\n## Citation\n\nIf you have used MONAI in your research, please cite us! The citation can be exported from: https://arxiv.org/abs/2211.02701.\n\n## Model Zoo\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\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\nJoin the conversation on Twitter/X [@ProjectMONAI](https://twitter.com/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- 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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