• MONAI (Stephen Aylward, Prerna Dogra, Jorge Cardoso)

  • 1) Introduction to MONAI and the MONAI Community - Stephen Aylward

    2) MONAI medical deep learning capabilities and roadmap - Jorge Cardoso 

    3) MONAI and clinical workflows: Clara and federated learning - Brad Genereaux

• Abstract: The MONAI framework is the open-source foundation for medical deep learning that began as a collaboration between King's College London and NVidia and that is now being led by Project MONAI. The MONAI framework is a freely available, community-supported, PyTorch-based framework for deep learning that is specifically designed for healthcare imaging AI. It is an extensible framework that is meant to include the state-of-the-art neural networks, data augmentation methods, and training systems for medical imaging.  The framework also includes streamlined access to medical imaging grand challenges and is designed to promote reproducibility and open science. Project MONAI is developing the MONAI framework and associated tutorials and course material as part of its commitment to creating an inclusive community of AI researchers for the development and exchange of best practices for AI in healthcare imaging, for academia and industry. Project MONAI and the MONAI framework would not have been possible without existing toolkits such as Nvidia Clara Train, NiftyNet, DLTK, and DeepNeuro and the communities that have grown around them.

• Special Edition: Imaging Data Commons Webinar (IDC team)

• NIAID TB Bioportal (Alex Rosenthal, Darrell Hurt)

• Orchestration of distributed image archives (Jonas De Almeida)
  
  Summary: Recent advances in the use of HTTP range requests to traverse bioformats*, coupled with a general move to “zero footprint” image informatics solutions, enable the creation of image archives as an exercise in governance. A particular feature of this configuration is that the images do not have to be copied or moved from their primary location. This has two interesting effects: a) the image owner remains in control of its governance; and b) training of AI classifiers can be federated across image sets that are not even shared.

  * Bremer E, Saltz J, Almeida JS. ImageBox 2 – Efficient and rapid access of image tiles from whole-slide images using serverless HTTP range requests. J Pathol Inform 2020;11:29

• MONAI (Stephen Aylward, Prerna Dogra, Jorge Cardoso)

  • Introduction to MONAI and the MONAI Community - Stephen Aylward

  • MONAI medical deep learning capabilities and roadmap - Jorge Cardoso 

  • MONAI and clinical workflows: Clara and federated learning - Brad Genereaux

• TCIA Update (Justin Kirby)

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• Kheops (Joël Spaltenstein, Osman Ratib)
• TCIA Update (Justin Kirby)

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• Computational Imaging for Precision Medicine: A quest for generalizable AI models  (Satish Viswanath)

• TCIA Update (John Freymann)

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• Distributed Learning of Deep Learning in Medical Imaging (Daniel Rubin)
• MedICI website (Benjamin Bearce)
• TCIA update (John Freymann)

• ACR's AI-LAB (Laura Coombs, Chris Treml)
• TCIA update (Justin Kirby)

• PathPresenter - a web-based digital pathology and image viewer (Rajendra Singh, Matthew Hanna)
• TCIA update (Justin Kirby)

• Medical Segmentation Decathlon: Generalizable 3D Semantic Segmentation (Amber Simpson)
• Imaging Data Commons Update (Todd Pihl)
• TCIA Update (Justin Kirby)

• Data Commons Overview (Todd Pihl)
• The Imaging Data Commons (Andrey Fedorov)
• TCIA Update (Justin Kirby)
• NBIA Update (Scott Gustafson)

• HistoQC: An Open-Source Quality Control Tool for Digital Pathology Slides (Andrew Janowczyk)
• RIL-Contour: a Medical Imaging Dataset Annotation Tool for and with Deep Learning (Kenneth Philbrick)
• TCIA Update (Justin Kirby)

• Advanced Methods in Tissue Cytometry (Rupert Ecker)
• Presentation by the 4D Necleome Imaging Working Group (David Grünwald)
• TCIA Update (Justin Kirby)

Joint Session with the CPTAC Special Interest Group

• CPTAC Project Overview (Chris Kinsinger)
• CPTAC Image Data at TCIA (Justin Kirby)
• CPTAC Proteomics Data at the Proteomics Data Commons (R. Rajesh Thangudu)
• CPTAC Genomic Data at the Genomics Data Commons (Ana Robles)
• Using the CPTAC Data Portal (R. Rajesh Thangudu)