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/X @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-1.4.dev2416.tar.gz (1.5 MB view details)

Uploaded Source

Built Distribution

monai_weekly-1.4.dev2416-py3-none-any.whl (1.4 MB view details)

Uploaded Python 3

File details

Details for the file monai_weekly-1.4.dev2416.tar.gz.

File metadata

  • Download URL: monai_weekly-1.4.dev2416.tar.gz
  • Upload date:
  • Size: 1.5 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.12.3

File hashes

Hashes for monai_weekly-1.4.dev2416.tar.gz
Algorithm Hash digest
SHA256 930351085ed361e973895dabb9795d61b736262e0cbeaaa117d6333393a02aca
MD5 489c4eb897f621071f78729c2c8cb0dc
BLAKE2b-256 b8159e363104055085e02e58c36060c4688183c4589ec3d370ad58e8d65b18d5

See more details on using hashes here.

Provenance

File details

Details for the file monai_weekly-1.4.dev2416-py3-none-any.whl.

File metadata

File hashes

Hashes for monai_weekly-1.4.dev2416-py3-none-any.whl
Algorithm Hash digest
SHA256 6c4c2958c6dfddc83a00e33f3e453f204eedad358c41d3472a4be6de346d4bb3
MD5 c826ae6f2a4d2b53fdf7a898acc90539
BLAKE2b-256 f23a70a0c9e9b3f4ccd1c139ac69b98f770a2c5205bcdd22c76079d3d163e4d9

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