Skip to main content

AI Toolkit for Healthcare Imaging

Project description

project-monai

Medical Open Network for AI

Supported Python versions License PyPI version docker conda

premerge postmerge docker Documentation Status codecov

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

Please see the technical highlights and What's New of the milestone releases.

  • 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 multi-node data parallelism support.

Installation

To install the current release, you can simply run:

pip install monai

Please refer to the installation guide for other installation options.

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.

Citation

If you have used MONAI in your research, please cite us! The citation can be exported from: https://arxiv.org/abs/2211.02701.

Model Zoo

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.

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

Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

monai-weekly-1.3.dev2326.tar.gz (1.4 MB view details)

Uploaded Source

Built Distribution

monai_weekly-1.3.dev2326-py3-none-any.whl (1.3 MB view details)

Uploaded Python 3

File details

Details for the file monai-weekly-1.3.dev2326.tar.gz.

File metadata

  • Download URL: monai-weekly-1.3.dev2326.tar.gz
  • Upload date:
  • Size: 1.4 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.11.4

File hashes

Hashes for monai-weekly-1.3.dev2326.tar.gz
Algorithm Hash digest
SHA256 c855d0d8f215eda30449ce3cfb2d8b00ba5b72d66a37be91e9bdb25c8ca4aa63
MD5 249d4c16f938942503a9f23d98ff16d3
BLAKE2b-256 0f844a7af14c3164d635b4d875fde1c5183e87214fc3576d2ea72488c410ca0f

See more details on using hashes here.

Provenance

File details

Details for the file monai_weekly-1.3.dev2326-py3-none-any.whl.

File metadata

File hashes

Hashes for monai_weekly-1.3.dev2326-py3-none-any.whl
Algorithm Hash digest
SHA256 8e154accbf6bd40f0ac0d30d5c086f425fb5357eec91ad97937148fd8eaaf7ca
MD5 1ed1d9de45f88cf6f0fe14c6f9de1f15
BLAKE2b-256 7e4bfc77a088e902b70d54b1d3871443c16ca4f2bd84842b5eb6f83ccc2f5926

See more details on using hashes here.

Provenance

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page