AI Toolkit for Healthcare Imaging
Project description
Medical Open Network for AI
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 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 master 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
- Website: https://monai.io/
- API documentation: https://docs.monai.io
- Code: https://github.com/Project-MONAI/MONAI
- Project tracker: https://github.com/Project-MONAI/MONAI/projects
- Issue tracker: https://github.com/Project-MONAI/MONAI/issues
- Wiki: https://github.com/Project-MONAI/MONAI/wiki
- Test status: https://github.com/Project-MONAI/MONAI/actions
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
Built Distribution
File details
Details for the file monai-weekly-0.5.dev2110.tar.gz.
File metadata
- Download URL: monai-weekly-0.5.dev2110.tar.gz
- Upload date:
- Size: 311.7 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.3.0 pkginfo/1.7.0 requests/2.25.1 setuptools/53.0.0 requests-toolbelt/0.9.1 tqdm/4.59.0 CPython/3.9.2
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 9dd1cc661ab347238e597d0973a99e38e45a7fe2864f923e45f8c8bb78c11323 |
|
| MD5 | 99557ccae5645e66b65d88ec3fbac5f4 |
|
| BLAKE2b-256 | 8ad1cfa5f9823ee1ec753af024ce45102117e35cbe62422aa6c7e5cc02769f03 |
File details
Details for the file monai_weekly-0.5.dev2110-py3-none-any.whl.
File metadata
- Download URL: monai_weekly-0.5.dev2110-py3-none-any.whl
- Upload date:
- Size: 416.6 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.3.0 pkginfo/1.7.0 requests/2.25.1 setuptools/53.0.0 requests-toolbelt/0.9.1 tqdm/4.59.0 CPython/3.9.2
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | ecf9987f8da22484db8f5467fc727cb8bec88a0059637bbbef0f6be38c64fb17 |
|
| MD5 | 39dde817e9f8c4eb454943d6935f6bbd |
|
| BLAKE2b-256 | 6f29f3da5a91e21dd9cd22693891430980ce904a45468676b46561ad35360bdd |
