This talk is part of a limited series co-presented by SBME, BMI&AI, and BC Translational DPI.
Talk: “Artificial Intelligence and digital pathology: dealing with the annotation bottleneck”
The introduction of scanners that are capable of digitizing microscopic slides at high magnification has led to an explosion of interest in computational pathology in general and deep learning applied to whole slide images (WSIs) in particular. In my lab at Sunnybrook, we are developing AI models that can detect cancer, automatically segment regions of interest, and learn predictive and prognostic models that can be used to guide treatment decisions. In this talk I will outline some of the unique challenges of working with these extremely large WSIs and discuss some of the approaches that we have developed to overcome the problems of sparse annotations and weak, noisy labels, including self-supervision and multiple instance learning. I will also outline some of the challenges in deploying AI algorithms to the clinic.
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