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MONAI

Key Investigators

• Stephen Aylward (Kitware)
• Matt McCormick (Kitware)
• Hans Johnson (The University of Iowa)

Project Description

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.

Objective

1. Introduce Monai
2. Datasets and DataLoaders for participating in Challenges and using pubic data collections
3. Transforms for data pre-processing and augmentation
4. Participating in a deep learning challenge in 10 lines of python
5. Integration into clinical workflows: MONAI + Nvidia CLARA
6. Ongoing efforts: Model Zoo

Approach and Plan

1. Present MONAI
2. Advertise resources for support and training (including resources for hackathons / datathons)

Progress and Next Steps

1. YouTube: 5-minute presentation on Monday
2. 1 hours presentation on Wednesday

Illustrations

1. 

Background and References

• Learn
  • Getting Started (Installation, Examples, Demos, etc.) https://monai.io/start.html
• Contribute
  • GitHub: https://github.com/Project-MONAI/MONAI
    • Community Guide: https://github.com/Project-MONAI/MONAI#community
    • Contributing Guide: https://github.com/Project-MONAI/MONAI#contributing
    • Issue Tracker: “Good First Issue” tag: https://github.com/Project-MONAI/MONAI/labels/good%20first%20issue
• Support
  • PyTorch Forums. Tag @monai or see the MONAI user page. https://discuss.pytorch.org/u/MONAI/
  • Stack Overflow. See existing tagged questions or create your own: https://stackoverflow.com/questions/tagged/monai
  • Join our Slack Channel. Fill out the Google Form here: https://forms.gle/QTxJq3hFictp31UM9