  Prostate cancer (PCa) is one of the most common and aggressive cancers
worldwide. The Gleason score (GS) system is the standard way of classifying
prostate cancer and the most reliable method to determine the severity and
treatment to follow. The pathologist looks at the arrangement of cancer cells
in the prostate and assigns a score on a scale that ranges from 6 to 10.
Automatic analysis of prostate whole-slide images (WSIs) is usually addressed
as a binary classification problem, which misses the finer distinction between
stages given by the GS. This paper presents a probabilistic deep learning
ordinal classification method that can estimate the GS from a prostate WSI.
Approaching the problem as an ordinal regression task using a differentiable
probabilistic model not only improves the interpretability of the results, but
also improves the accuracy of the model when compared to conventional deep
classification and regression architectures.

Computer Vision and Pattern Recognition