AI Toolkit for Healthcare Imaging


Keywords
deep-learning, healthcare-imaging, medical-image-computing, medical-image-processing, monai, python3, pytorch
License
Apache-2.0
Install
pip install monai==1.3.2rc1

Documentation

project-monai

Medical Open Network for AI

Supported Python versions License PyPI version docker conda

premerge postmerge Documentation Status codecov

MONAI is a PyTorch-based, open-source framework for deep learning in healthcare imaging, part of PyTorch Ecosystem. Its ambitions are:

  • 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 and What'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.

Installation

To install the current release, you can simply run:

pip install monai

Please refer to the installation guide for other installation options.

Getting Started

MedNIST demo and MONAI for PyTorch Users are available on Colab.

Examples and notebook tutorials are located at Project-MONAI/tutorials.

Technical documentation is available at 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 is a place for researchers and data scientists to share the latest and great models from the community. Utilizing the MONAI Bundle format makes it easy to get started building workflows with MONAI.

Contributing

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

Community

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

Ask and answer questions over on MONAI's GitHub Discussions tab.

Links