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 1 month 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: 2025-01-24T15:00:48.000Z (3 months ago)
• Last Synced: 2025-01-25T18:33:57.470Z (3 months ago)
• Topics: deep-learning, healthcare-imaging, medical-image-computing, medical-image-processing, monai, python3, pytorch
• Language: Python
• Homepage: https://monai.io/
• Size: 67 MB
• Stars: 6,056
• Watchers: 89
• Forks: 1,118
• Open Issues: 392
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md
  • Citation: CITATION.cff

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README

        


  



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

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

[![License](https://img.shields.io/badge/license-Apache%202.0-green.svg)](https://opensource.org/licenses/Apache-2.0)

[![PyPI version](https://badge.fury.io/py/monai.svg)](https://badge.fury.io/py/monai)

[![docker](https://img.shields.io/badge/docker-pull-green.svg?logo=docker&logoColor=white)](https://hub.docker.com/r/projectmonai/monai)

[![conda](https://img.shields.io/conda/vn/conda-forge/monai?color=green)](https://anaconda.org/conda-forge/monai)

[![premerge](https://github.com/Project-MONAI/MONAI/actions/workflows/pythonapp.yml/badge.svg?branch=dev)](https://github.com/Project-MONAI/MONAI/actions/workflows/pythonapp.yml)

[![postmerge](https://img.shields.io/github/checks-status/project-monai/monai/dev?label=postmerge)](https://github.com/Project-MONAI/MONAI/actions?query=branch%3Adev)

[![Documentation Status](https://readthedocs.org/projects/monai/badge/?version=latest)](https://docs.monai.io/en/latest/)

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[![monai Downloads Last Month](https://assets.piptrends.com/get-last-month-downloads-badge/monai.svg 'monai Downloads Last Month by pip Trends')](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: .

## 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: 

- API documentation (milestone): 

- API documentation (latest dev): 

- Code: 

- Project tracker: 

- Issue tracker: 

- Wiki: 

- Test status: 

- PyPI package: 

- conda-forge: 

- Weekly previews: 

- Docker Hub: