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 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:

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

For more details, please refer to the installation guide.

Getting Started

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

Examples are located at monai/examples, 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.

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