  Automated grading of prostate cancer histopathology images is a challenging
task, with one key challenge being the scarcity of annotations down to the
level of regions of interest (strong labels), as typically the prostate cancer
Gleason score is known only for entire tissue slides (weak labels). In this
study, we focus on automated Gleason score assignment of prostate cancer
whole-slide images on the basis of a large weakly-labeled dataset and a smaller
strongly-labeled one. We efficiently leverage information from both label
sources by jointly training a classifier on the two datasets and by introducing
a gradient update scheme that assigns different relative importances to each
training example, as a means of self-controlling the weak supervision signal.
Our approach achieves superior performance when compared with standard Gleason
scoring methods.
