# Gleason Grading of Histology Prostate Images through Semantic Segmentation via Residual U-Net Source: [Gleason Grading of Histology Prostate Images through Semantic Segmentation via Residual U-Net]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:18:12.808Z Authors: Amartya Kalapahar, Julio Silva-Rodr\\'iguez, Adri\\'an Colomer, Fernando L\\'opez-Mir, Valery Naranjo Published: 2020-05-22T19:49:10.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()