  Worldwide, prostate cancer is one of the main cancers affecting men. The
final diagnosis of prostate cancer is based on the visual detection of Gleason
patterns in prostate biopsy by pathologists. Computer-aided-diagnosis systems
allow to delineate and classify the cancerous patterns in the tissue via
computer-vision algorithms in order to support the physicians' task. The
methodological core of this work is a U-Net convolutional neural network for
image segmentation modified with residual blocks able to segment cancerous
tissue according to the full Gleason system. This model outperforms other
well-known architectures, and reaches a pixel-level Cohen's quadratic Kappa of
0.52, at the level of previous image-level works in the literature, but
providing also a detailed localisation of the patterns.

Computer Vision and Pattern Recognition;Image and Video Processing