  Prostate cancer is one of the main diseases affecting men worldwide. The gold
standard for diagnosis and prognosis is the Gleason grading system. In this
process, pathologists manually analyze prostate histology slides under
microscope, in a high time-consuming and subjective task. In the last years,
computer-aided-diagnosis (CAD) systems have emerged as a promising tool that
could support pathologists in the daily clinical practice. Nevertheless, these
systems are usually trained using tedious and prone-to-error pixel-level
annotations of Gleason grades in the tissue. To alleviate the need of manual
pixel-wise labeling, just a handful of works have been presented in the
literature. Motivated by this, we propose a novel weakly-supervised
deep-learning model, based on self-learning CNNs, that leverages only the
global Gleason score of gigapixel whole slide images during training to
accurately perform both, grading of patch-level patterns and biopsy-level
scoring. To evaluate the performance of the proposed method, we perform
extensive experiments on three different external datasets for the patch-level
Gleason grading, and on two different test sets for global Grade Group
prediction. We empirically demonstrate that our approach outperforms its
supervised counterpart on patch-level Gleason grading by a large margin, as
well as state-of-the-art methods on global biopsy-level scoring. Particularly,
the proposed model brings an average improvement on the Cohen's quadratic kappa
(k) score of nearly 18% compared to full-supervision for the patch-level
Gleason grading task.
