  Prostate cancer is one of the main diseases affecting men worldwide. The
Gleason scoring system is the primary diagnostic tool for prostate cancer. This
is obtained via the visual analysis of cancerous patterns in prostate biopsies
performed by expert pathologists, and the aggregation of the main Gleason
grades in a combined score. Computer-aided diagnosis systems allow to reduce
the workload of pathologists and increase the objectivity. Recently, efforts
have been made in the literature to develop algorithms aiming the direct
estimation of the global Gleason score at biopsy/core level with global labels.
However, these algorithms do not cover the accurate localization of the Gleason
patterns into the tissue. In this work, we propose a deep-learning-based system
able to detect local cancerous patterns in the prostate tissue using only the
global-level Gleason score during training. The methodological core of this
work is the proposed weakly-supervised-trained convolutional neural network,
WeGleNet, based on a multi-class segmentation layer after the feature
extraction module, a global-aggregation, and the slicing of the background
class for the model loss estimation during training. We obtained a Cohen's
quadratic kappa (k) of 0.67 for the pixel-level prediction of cancerous
patterns in the validation cohort. We compared the model performance for
semantic segmentation of Gleason grades with supervised state-of-the-art
architectures in the test cohort. We obtained a pixel-level k of 0.61 and a
macro-averaged f1-score of 0.58, at the same level as fully-supervised methods.
Regarding the estimation of the core-level Gleason score, we obtained a k of
0.76 and 0.67 between the model and two different pathologists. WeGleNet is
capable of performing the semantic segmentation of Gleason grades similarly to
fully-supervised methods without requiring pixel-level annotations.
