Skip to content

Project-MONAI/MONAI

This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.

Folders and files

NameName
Last commit message
Last commit date

Latest commit

Jan 19, 2021
fdd0c2d · Jan 19, 2021
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

project-monai

Medical Open Network for AI

License CI Build Documentation Status codecov PyPI version

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

pip install monai

Installing the master branch from the source code repository:

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

Using the pre-built Docker image 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 and 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.

Community

Join the conversation on Twitter @ProjectMONAI or join our Slack channel.

Ask and answer questions over on MONAI's GitHub Discussions tab.

Links