AI Toolkit for Healthcare Imaging
Project description
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.
Tutorials & examples are located at monai/examples.
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
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