Monai
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Monai | 4,697 | | 42 | 7 hours ago | 71 | June 13, 2022 | 320 | apache-2.0 | Python | |
| AI Toolkit for Healthcare Imaging | ||||||||||
| Docproduct | 511 | 2 years ago | 1 | June 06, 2019 | 22 | mit | Jupyter Notebook | |||
| Medical Q&A with Deep Language Models | ||||||||||
| Hi Ml | 180 |  | 1 | 2 months ago | 14 | July 04, 2022 | 98 | mit | Python | |
| HI-ML toolbox for deep learning for medical imaging and Azure integration | ||||||||||
| Fuse Med Ml | 110 | 19 days ago | 34 | apache-2.0 | Python | |||||
| A python framework accelerating ML based discovery in the medical field by encouraging code reuse. Batteries included :) | ||||||||||
| Ai_for_healthcare | 95 | 5 years ago | 1 | Jupyter Notebook | ||||||
| This is the code for "AI for Healthcare" By Siraj Raval on Youtube | ||||||||||
| Monai Deploy | 83 | 4 days ago | 24 | apache-2.0 | Shell | |||||
| MONAI Deploy aims to become the de-facto standard for developing, packaging, testing, deploying and running medical AI applications in clinical production. | ||||||||||
| Monai Deploy App Sdk | 71 | a month ago | 10 | April 22, 2022 | 59 | apache-2.0 | Jupyter Notebook | |||
| MONAI Deploy App SDK offers a framework and associated tools to design, develop and verify AI-driven applications in the healthcare imaging domain. | ||||||||||
| Generative Ai Pharmacist | 58 | 2 months ago | ||||||||
| Generative AI Pharmacist (For Demo Purposes Only) | ||||||||||
| Mri Analysis Pytorch | 52 | 5 years ago | 1 | Jupyter Notebook | ||||||
| MRI analysis using PyTorch and MedicalTorch | ||||||||||
| Awesome Healthmetrics | 52 | 7 days ago | mit | |||||||
| A curated list of awesome resources at the intersection of healthcare and AI | ||||||||||
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
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 @ProjectMONAI or join our Slack channel.
Ask and answer questions over on MONAI's GitHub Discussions tab.
Links
- Website: https://monai.io/
- API documentation (milestone): https://docs.monai.io/
- API documentation (latest dev): https://docs.monai.io/en/latest/
- Code: 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
- PyPI package: https://pypi.org/project/monai/
- conda-forge: https://anaconda.org/conda-forge/monai
- Weekly previews: https://pypi.org/project/monai-weekly/
- Docker Hub: https://hub.docker.com/r/projectmonai/monai