Skip to main content

AI Toolkit for Healthcare Imaging

Project description

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 and What's New 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

To install the current release, you can simply run:

pip install monai

For other installation methods (using the default GitHub branch, using Docker, etc.), 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

Project details


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-0.6.dev2119.tar.gz (378.3 kB view details)

Uploaded Source

Built Distribution

monai_weekly-0.6.dev2119-py3-none-any.whl (502.3 kB view details)

Uploaded Python 3

File details

Details for the file monai-weekly-0.6.dev2119.tar.gz.

File metadata

  • Download URL: monai-weekly-0.6.dev2119.tar.gz
  • Upload date:
  • Size: 378.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.5

File hashes

Hashes for monai-weekly-0.6.dev2119.tar.gz
Algorithm Hash digest
SHA256 a6904c94dc9582b958006136a36ca70500d52c25ad42c785823c4f3fd380ec35
MD5 793ccd5ad9037a055cfc0cf2610691a3
BLAKE2b-256 f1b2487d8a2f9178aba27a8113738cfa060967b9b1d014142d33cc6115137d1a

See more details on using hashes here.

File details

Details for the file monai_weekly-0.6.dev2119-py3-none-any.whl.

File metadata

  • Download URL: monai_weekly-0.6.dev2119-py3-none-any.whl
  • Upload date:
  • Size: 502.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.5

File hashes

Hashes for monai_weekly-0.6.dev2119-py3-none-any.whl
Algorithm Hash digest
SHA256 55cfd545f402de19c99db614bce16dfd7a986b42ee69ac55aece02d44e1d630c
MD5 674279674d3ae7d7a1cc9cc0a648bc16
BLAKE2b-256 8e7d44b7011f326fc681606daee1a1bb180193ba22a2934c1db9cb8a483f4b56

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