Can We Stop Annotating? Weakly Supervised AI For Pathology With Geert Litjens, Radboudumc


Have you ever wondered what semi-supervised, weekly, and unsupervised artificial intelligence digital pathology models can do to help pathologists?

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?

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