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Specialized Domains","Multimodal \u0026 Imaging AI","🧬 Biology \u0026 Medicine","Software tools"],"readme":"\u003cp align=\"center\"\u003e\n  \u003cimg src=\"https://raw.githubusercontent.com/Project-MONAI/MONAI/dev/docs/images/MONAI-logo-color.png\" width=\"50%\" alt='project-monai'\u003e\n\u003c/p\u003e\n\n**M**edical **O**pen **N**etwork for **AI**\n\n![Supported Python versions](https://raw.githubusercontent.com/Project-MONAI/MONAI/dev/docs/images/python.svg)\n[![License](https://img.shields.io/badge/license-Apache%202.0-green.svg)](https://opensource.org/licenses/Apache-2.0)\n[![PyPI version](https://badge.fury.io/py/monai.svg)](https://badge.fury.io/py/monai)\n[![docker](https://img.shields.io/badge/docker-pull-green.svg?logo=docker\u0026logoColor=white)](https://hub.docker.com/r/projectmonai/monai)\n[![conda](https://img.shields.io/conda/vn/conda-forge/monai?color=green)](https://anaconda.org/conda-forge/monai)\n\n[![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)\n[![postmerge](https://img.shields.io/github/checks-status/project-monai/monai/dev?label=postmerge)](https://github.com/Project-MONAI/MONAI/actions?query=branch%3Adev)\n[![Documentation Status](https://readthedocs.org/projects/monai/badge/?version=latest)](https://docs.monai.io/en/latest/)\n[![codecov](https://codecov.io/gh/Project-MONAI/MONAI/branch/dev/graph/badge.svg?token=6FTC7U1JJ4)](https://codecov.io/gh/Project-MONAI/MONAI)\n[![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)\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\u003e _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 \u0026 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## 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: \u003chttps://arxiv.org/abs/2211.02701\u003e.\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) 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: \u003chttps://monai.io/\u003e\n- API documentation (milestone): \u003chttps://docs.monai.io/\u003e\n- API documentation (latest dev): \u003chttps://docs.monai.io/en/latest/\u003e\n- Code: \u003chttps://github.com/Project-MONAI/MONAI\u003e\n- Project tracker: \u003chttps://github.com/Project-MONAI/MONAI/projects\u003e\n- Issue tracker: \u003chttps://github.com/Project-MONAI/MONAI/issues\u003e\n- Wiki: \u003chttps://github.com/Project-MONAI/MONAI/wiki\u003e\n- Test status: \u003chttps://github.com/Project-MONAI/MONAI/actions\u003e\n- PyPI package: \u003chttps://pypi.org/project/monai/\u003e\n- conda-forge: \u003chttps://anaconda.org/conda-forge/monai\u003e\n- Weekly previews: \u003chttps://pypi.org/project/monai-weekly/\u003e\n- Docker Hub: \u003chttps://hub.docker.com/r/projectmonai/monai\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FProject-MONAI%2FMONAI","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FProject-MONAI%2FMONAI","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FProject-MONAI%2FMONAI/lists"}