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

arXiv: https://arxiv.org/abs/2005.11368 · HTML: https://mlsanity.com/paper/2005.11368/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2020.

## Built on by (most cited first, 5 of 5)

- [WeGleNet: A Weakly-Supervised Convolutional Neural Network for the Semantic Segmentation of Gleason Grades in Prostate Histology Images](https://mlsanity.com/paper/2105.10445/) (2021)
- [A review of machine learning approaches, challenges and prospects for computational tumor pathology](https://mlsanity.com/paper/2206.01728/) (2022)
- [Self-supervised learning of a tailored Convolutional Auto Encoder for histopathological prostate grading](https://mlsanity.com/paper/2303.11837/) (2023)
- [Acoustic Echo Cancellation using Residual U-Nets](https://mlsanity.com/paper/2109.09686/) (2021)
- [Automated segmentation and morphological characterization of placental histology images based on a single labeled image](https://mlsanity.com/paper/2210.03566/) (2022)

## Abstract (arXiv)

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.