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MedicalOpenNetwork forAI

MONAI is aPyTorch-based,open-source framework for deep learning in healthcare imaging, part of thePyTorch 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.

Please seethe technical highlights andWhat's New 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.

To installthe current release, you can simply run:

pip install monai

Please refer tothe installation guide for other installation options.

MedNIST demo andMONAI for PyTorch Users are available on Colab.

Examples and notebook tutorials are located atProject-MONAI/tutorials.

Technical documentation is available atdocs.monai.io.

If you have used MONAI in your research, please cite us! The citation can be exported from:https://arxiv.org/abs/2211.02701.

The MONAI Model Zoo is a place for researchers and data scientists to share the latest and great models from the community.Utilizingthe MONAI Bundle format makes it easy toget started building workflows with MONAI.

For guidance on making a contribution to MONAI, see thecontributing guidelines.

Join the conversation on Twitter/X@ProjectMONAI or join ourSlack channel.

Ask and answer questions over onMONAI's GitHub Discussions tab.

• 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