Project MONAI

Medical Open Network for AI

MONAI is a PyTorch-based, open-source platform for deep learning in healthcare imaging. 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.

• 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

Clone and build this repository from source

git clone https://github.com/Project-MONAI/MONAI.git
pip install -e MONAI/

Run some of the examples in Getting Started

Getting Started

Tutorials & examples are located at monai/examples.

Technical documentation is available via Read the Docs.

Contributing

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

Links

• Website: (coming soon)
• API documentation: https://monai.readthedocs.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://gitlab.com/project-monai/MONAI/pipelines