Fine-Grained Deep Learning for Gleason Grading in Prostate Histopathology

DOI

10.2991/978-94-6239-648-7_64How to use a DOI?

Keywords

Fine-grained Image Classification; Deep Learning; Prostate Cancer; Histopathological Images; Gleason Grading

Abstract

One of the most frequent cancers in male patients is prostate cancer, diagnosis and prognosis of which include histopathological image analysis, especially Gleason grading. It is a delicate activity that involves identifying subtle architectural variations in tissue patterns which are however disadvantaged by inter-observer imprecision, subjectiveness, and labor intensity. Deep learning has become a powerful solution, offering an automated, reproducible grading architecture modelling intra-class differences and visual similarities. This survey discusses recent state-of-the-art deep learning methods in the field of fine-grained prostate cancer histopathology, which are whole-slide image analysis and Gleason pattern recognition. It studies convolutional neural networks, attention mechanisms and vision transformers with regard to their capability of localizing diagnostic regions and multi-scale context integration. The weakly supervised learning and transfer learning, as well as interpretation techniques designed to minimize the annotation need and promote clinical trust, are also discussed in the paper. Such benchmark datasets as The Cancer Genome Atlas (TCGA) and measures as quadratic Cohen’s kappa and area under the curve (AUC) are examined. Continuous problems are the staining discrepancy, unclear labels, and computational needs. Prospective clinical validation remains crucial for future translation.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Kaixin Chen
PY  - 2026
DA  - 2026/04/24
TI  - Fine-Grained Deep Learning for Gleason Grading in Prostate Histopathology
BT  - Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)
PB  - Atlantis Press
SP  - 583
EP  - 595
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-648-7_64
DO  - 10.2991/978-94-6239-648-7_64
ID  - Chen2026
ER  -