Medical Open Network for AI

MONAI is a PyTorch-based, open-source framework 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

To install the current release:

pip install monai

To install from the source code repository:

pip install git+https://github.com/Project-MONAI/MONAI#egg=MONAI

Alternatively, pre-built Docker image is available via DockerHub:

# with docker v19.03+
docker run --gpus all --rm -ti --ipc=host projectmonai/monai:latest

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: https://monai.io/
• 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://github.com/Project-MONAI/MONAI/actions