monai 0.8.1

AI Toolkit for Healthcare Imaging

Medical Open Network for AI

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

The codebase is currently under active development. Please see the technical highlights and What's New of the current milestone release.

• 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 data parallelism support.

Installation

To install the current release, you can simply run:

pip install monai

For other installation methods (using the default GitHub branch, using Docker, etc.), please refer to the installation guide.

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.

Contributing

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

Community

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

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

Links

• Website: https://monai.io/
• API documentation: https://docs.monai.io
• 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/
• Weekly previews: https://pypi.org/project/monai-weekly/
• Docker Hub: https://hub.docker.com/r/projectmonai/monai