  The Gleason scoring system is the primary diagnostic and prognostic tool for
prostate cancer. In recent years, with the development of digitisation devices,
the use of computer vision techniques for the analysis of biopsies has
increased. However, to the best of the authors' knowledge, the development of
algorithms to automatically detect individual cribriform patterns belonging to
Gleason grade 4 has not yet been studied in the literature. The objective of
the work presented in this paper is to develop a deep-learning-based system
able to support pathologists in the daily analysis of prostate biopsies. The
methodological core of this work is a patch-wise predictive model based on
convolutional neural networks able to determine the presence of cancerous
patterns. In particular, we train from scratch a simple self-design
architecture. The cribriform pattern is detected by retraining the set of
filters of the last convolutional layer in the network. From the reconstructed
prediction map, we compute the percentage of each Gleason grade in the tissue
to feed a multi-layer perceptron which provides a biopsy-level score.mIn our
SICAPv2 database, composed of 182 annotated whole slide images, we obtained a
Cohen's quadratic kappa of 0.77 in the test set for the patch-level Gleason
grading with the proposed architecture trained from scratch. Our results
outperform previous ones reported in the literature. Furthermore, this model
reaches the level of fine-tuned state-of-the-art architectures in a
patient-based four groups cross validation. In the cribriform pattern detection
task, we obtained an area under ROC curve of 0.82. Regarding the biopsy Gleason
scoring, we achieved a quadratic Cohen's Kappa of 0.81 in the test subset.
Shallow CNN architectures trained from scratch outperform current
state-of-the-art methods for Gleason grades classification.
