# MONAI

**Repository Path**: dogeblog/MONAI

## Basic Information

- **Project Name**: MONAI
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: dev
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-02-27
- **Last Updated**: 2025-02-27

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

<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**

![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>