AI Toolkit for Healthcare Imaging
Project description
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:
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: (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://github.com/Project-MONAI/MONAI/actions
Release history Release notifications | RSS feed
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