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.dev2417.tar.gz (1.5 MB view details)

Uploaded Source

Built Distribution

monai_weekly-1.4.dev2417-py3-none-any.whl (1.4 MB view details)

Uploaded Python 3

File details

Details for the file monai_weekly-1.4.dev2417.tar.gz.

File metadata

  • Download URL: monai_weekly-1.4.dev2417.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.dev2417.tar.gz
Algorithm Hash digest
SHA256 65f63d6b82ad184f9a99fde3e41170bfd264b35e0626dd84e2e005784ae778c4
MD5 fcd19338521d6111c9387acf2eb85234
BLAKE2b-256 8725a166a00a8c9d06a998259f2c71a1b07b58725089341499bb1d3d065960d8

See more details on using hashes here.

Provenance

File details

Details for the file monai_weekly-1.4.dev2417-py3-none-any.whl.

File metadata

File hashes

Hashes for monai_weekly-1.4.dev2417-py3-none-any.whl
Algorithm Hash digest
SHA256 3d1f7134cb747b6921951a0be65ec68bbb8938b9394b084beaf6a3f27d4fde97
MD5 b73b950428079d6e0075ad71a01305c0
BLAKE2b-256 3785ad9609401b67f99c0a6b778cfd39358ba3cf175c3c3a0f3e4934d7359805

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