This release is a pre-release and may not be stable for production use.
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
1 maintainer
Release files for monai-weekly 0.5.dev2105
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| monai-weekly-0.5.dev2105.tar.gz | 283.0 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| monai_weekly-0.5.dev2105-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 669.0 kB
Release files / monai-weekly-0.5.dev2105.tar.gz
| Download URL | monai-weekly-0.5.dev2105.tar.gz |
|---|---|
| Size | 283.0 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
d09ae73a8728d0d94518e846bb43e60141d7f3affc80d0538380826684957d57
|
|
BLAKE2b-256 checksum How to use checksums |
154872caf02577212a0bcc102951f1334f1cd7b1be191c5066f1821399168526
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.3.0 pkginfo/1.7.0 requests/2.25.1 setuptools/52.0.0 requests-toolbelt/0.9.1 tqdm/4.56.0 CPython/3.8.7
|
Release files / monai_weekly-0.5.dev2105-py3-none-any.whl
| Download URL | monai_weekly-0.5.dev2105-py3-none-any.whl |
|---|---|
| Size | 386.0 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
5b1d4ee71af9f1216cfab0deda0d6bfcc355dc25500ae0ac248263e00b20c710
|
|
BLAKE2b-256 checksum How to use checksums |
58978d281407f4058dbc5149745fcfe8d2eb0f13a71c760f34a5e23e3433caf6
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.3.0 pkginfo/1.7.0 requests/2.25.1 setuptools/52.0.0 requests-toolbelt/0.9.1 tqdm/4.56.0 CPython/3.8.7
|
