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

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.

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-0.9.dev2224.tar.gz (724.8 kB view details)

Uploaded Source

Built Distribution

monai_weekly-0.9.dev2224-py3-none-any.whl (939.8 kB view details)

Uploaded Python 3

File details

Details for the file monai-weekly-0.9.dev2224.tar.gz.

File metadata

  • Download URL: monai-weekly-0.9.dev2224.tar.gz
  • Upload date:
  • Size: 724.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.13

File hashes

Hashes for monai-weekly-0.9.dev2224.tar.gz
Algorithm Hash digest
SHA256 098542aad7bc1c11213e3af69bbb82720c97af45bcc1b2993e71e57eee61cd4e
MD5 54cc73d3114a14cdd01a17c277dfd29c
BLAKE2b-256 3f08bbb366bf38b7958ff9237c75406b0c2806e7eb163bbf8278402544b8ccb9

See more details on using hashes here.

File details

Details for the file monai_weekly-0.9.dev2224-py3-none-any.whl.

File metadata

File hashes

Hashes for monai_weekly-0.9.dev2224-py3-none-any.whl
Algorithm Hash digest
SHA256 6836f6b4cf1a261f0604899f6a8e3b3919be3b21e50ef05889fba911769f1a1e
MD5 b694fd282ee008b000f74c8ed2dc9a4c
BLAKE2b-256 a1ecc60ecef8d3700f51b5ebbcd6780035932df1093c373a33d1f9b8912fea4f

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