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 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-1.2.dev2311.tar.gz (1.3 MB view details)

Uploaded Source

Built Distribution

monai_weekly-1.2.dev2311-py3-none-any.whl (1.2 MB view details)

Uploaded Python 3

File details

Details for the file monai-weekly-1.2.dev2311.tar.gz.

File metadata

  • Download URL: monai-weekly-1.2.dev2311.tar.gz
  • Upload date:
  • Size: 1.3 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.11.2

File hashes

Hashes for monai-weekly-1.2.dev2311.tar.gz
Algorithm Hash digest
SHA256 c9995ed26371778151630891d4ef95c413058410d5e10706931d803ad1be25cf
MD5 ef8ef20d553673957b3b3bfbe4a0529a
BLAKE2b-256 895f1eb9a8309fc557a728a6ce0fd0fcfad66bb5038a15d92d0524595a5363a2

See more details on using hashes here.

File details

Details for the file monai_weekly-1.2.dev2311-py3-none-any.whl.

File metadata

File hashes

Hashes for monai_weekly-1.2.dev2311-py3-none-any.whl
Algorithm Hash digest
SHA256 814b470f75dcf6dd1064e2db0cb87d2ab22add9c1fcd53345d180d675fdde0b3
MD5 abe3d09a99eae82e115e8d789f08407f
BLAKE2b-256 c1e290468b24fb455a26a6dd1ab7981de1cec5aca70602609fc0de210ca1df55

See more details on using hashes here.

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