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Automated grading of prostate cancer histopathology images is a challengingtask, with one key challenge being the scarcity of annotations down to thelevel of regions of interest (strong labels), as typically the prostate cancerGleason score is known only for entire tissue slides (weak labels). In thisstudy, we focus on automated Gleason score assignment of prostate cancerwhole-slide images on the basis of a large weakly-labeled dataset and a smallerstrongly-labeled one. We efficiently leverage information from both labelsources by jointly training a classifier on the two datasets and by introducinga gradient update scheme that assigns different relative importances to eachtraining example, as a means of self-controlling the weak supervision signal.Our approach achieves superior performance when compared with standard Gleasonscoring methods.

Coupling weak and strong supervision for classification of prostate cancer histopathology images