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Worldwide, prostate cancer is one of the main cancers affecting men. Thefinal diagnosis of prostate cancer is based on the visual detection of Gleasonpatterns in prostate biopsy by pathologists. Computer-aided-diagnosis systemsallow to delineate and classify the cancerous patterns in the tissue viacomputer-vision algorithms in order to support the physicians' task. Themethodological core of this work is a U-Net convolutional neural network forimage segmentation modified with residual blocks able to segment canceroustissue according to the full Gleason system. This model outperforms otherwell-known architectures, and reaches a pixel-level Cohen's quadratic Kappa of0.52, at the level of previous image-level works in the literature, butproviding also a detailed localisation of the patterns.

Gleason Grading of Histology Prostate Images through Semantic Segmentation via Residual U-Net