Medical Open Network for AI
MONAI is a PyTorch-based, open-source framework for deep learning in healthcare imaging, part of the 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 an optimized and standardized way to create and evaluate deep learning models.
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.
MedNIST demo and MONAI for PyTorch Users are available on Colab.
Examples and notebook tutorials are located at Project-MONAI/tutorials.
Technical documentation is available at docs.monai.io.
The MONAI Model Zoo is a place for researchers and data scientists to share the latest and great models from the community. Utilizing the MONAI Bundle format makes it easy to get started building workflows with MONAI.
API documentation (milestone): https://docs.monai.io/
API documentation (latest dev): https://docs.monai.io/en/latest/
Project tracker: Project-MONAI/MONAI
Issue tracker: Project-MONAI/MONAI#issues
Test status: Project-MONAI/MONAI
PyPI package: https://pypi.org/project/monai/
Weekly previews: https://pypi.org/project/monai-weekly/
Docker Hub: https://hub.docker.com/r/projectmonai/monai