This workshop will focus on the latest and greatest Deep Learning methodologies for medical image analysis. We will present and discuss the methods that have shown most robustness - over multiple tasks, across multiple institutes; along with the newest models being proposed today. In addition to network development, attention needs to be given to the training and testing methods used, to reach standardized rigorous evaluation. In the afternoon program, we will focus on how to turn an algorithm into a product, and translate it into the clinic. We will have invited speakers who will share their experiences in moving from research to development and entrepreneurship.

CODE: u5959


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[1] Organizers

Hayit Greenspan
Tel Aviv University

Mads Nielsen
University of Copenhagen

Sarah Gerard
Harvard Medical School

Reinhard Beichel
College of Engineering, The University of Iowa

Bram van Ginneken
Radboud University Medical Center, Nijmegen, The Netherlands

[2] Topics will include:

  • DL methods for detection, segmentation and categorization
  • Designing DL models to address more advanced medical diagnosis support tasks (e.g., staging of disease, prediction of treatment response)
  • Methods for robust evaluation of DL solutions – towards high confidence solutions
  • From algorithms to products: what does it take to make sure patients benefit?
  • Deep learning app platforms, starting your own company, how to deal with regulatory issues

Invited Speakers

Rene Vidal

Johns Hopkins University

Marleen de Bruijne

Erasmus MC Rotterdam, University of Copenhagen

Bram van Ginneken

"Significant impact on healthcare: How to create a successful product from your research"
Radboud University Medical Center, Nijmegen

Meindert Niemeijer

"Productizing Autonomous AI: How to bring a diagnostic AI algorithm to the clinic"
IDx Technologies

Elliot Swart

3Derm Systems

Esther Abels

"Use Alliances, regulatory science and regulations to speed up bringing innovation to clinical market"
PathAI