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https://github.com/project-monai/monai

AI Toolkit for Healthcare Imaging
https://github.com/project-monai/monai

deep-learning healthcare-imaging medical-image-computing medical-image-processing monai python3 pytorch

Last synced: about 11 hours ago
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AI Toolkit for Healthcare Imaging

Host: GitHub
URL: https://github.com/project-monai/monai
Owner: Project-MONAI
License: apache-2.0
Created: 2019-10-11T16:41:38.000Z (over 5 years ago)
Default Branch: dev
Last Pushed: 2024-05-22T17:49:16.000Z (8 months ago)
Last Synced: 2024-05-22T21:22:50.897Z (8 months ago)
Topics: deep-learning, healthcare-imaging, medical-image-computing, medical-image-processing, monai, python3, pytorch
Language: Python
Homepage: https://monai.io/
Size: 63.5 MB
Stars: 5,404
Watchers: 90
Forks: 979
Open Issues: 329
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.cff

Awesome Lists containing this project

README

        


  



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

![Supported Python versions](https://raw.githubusercontent.com/Project-MONAI/MONAI/dev/docs/images/python.svg)

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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://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