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Have you ever wondered what semi-supervised, weekly, and unsupervised artificial intelligence digital pathology models can do to help pathologists?***Can we finally stop annotating???***This episode's guest Geert Litjens )- a member of the computational pathology group at Radboud University Medical Center explains how semi-supervised and weekly supervised artificial intelligence-based image analysis can help pathologists do better, more time-efficient, and data-efficient digital pathology. The supervised deep learning image analysis methods are used often and are well accepted in the digital pathology scientific community, however, they rely heavily on whole slide image annotations. This is very time-consuming and is subjected to annotator to annotator variability. There has been a lot of research going on in the computational pathology community on the semi and weakly supervised approaches. It turns out that those approaches are starting to match the results delivered by the supervised approaches. Are we there yet? Can we stop annotating pathology slides altogether and rely on the slide-level labels? Listen to the full episode to learn more + share with friends!This episodes resources:

• Aiosyn website)
• StreamingCNN)
• Pathology streaming pipeline)
• Streaming  CNNs for Multi-Megapixel Images (article))
• DALL-E-2 network that generates artworks from descriptions in natural language)

Other podcast episodes you'll enjoy:

• Bigpicture - the largest whole slide repository for AI model development in pathology. Where do we stand at month 15/72?)

• 5 Ways to make histopathology image models more robust to domain shift w/ Heather Couture, Pixel Scientia Labs)

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