# Self-learning for weakly supervised Gleason grading of local patterns

arXiv: https://arxiv.org/abs/2105.10420 · HTML: https://mlsanity.com/paper/2105.10420/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 41 references that are in the map.
- First posted on arXiv 21 May 2021.

## Builds on (most cited first, 15 of 15)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Big Self-Supervised Models are Strong Semi-Supervised Learners](https://mlsanity.com/paper/2006.10029/) (2020)
- [Self-training with Noisy Student improves ImageNet classification](https://mlsanity.com/paper/1911.04252/) (2019)
- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2004.04581/) (2020)
- [Billion-scale semi-supervised learning for image classification](https://mlsanity.com/paper/1905.00546/) (2019)
- [FickleNet: Weakly and Semi-supervised Semantic Image Segmentation using Stochastic Inference](https://mlsanity.com/paper/1902.10421/) (2019)
- [Learning From Noisy Large-Scale Datasets With Minimal Supervision](https://mlsanity.com/paper/1701.01619/) (2017)
- [Machine learning methods for histopathological image analysis](https://mlsanity.com/paper/1709.00786/) (2017)
- [Automated Gleason Grading of Prostate Biopsies using Deep Learning](https://mlsanity.com/paper/1907.07980/) (2019)
- [Self-Supervised Relational Reasoning for Representation Learning](https://mlsanity.com/paper/2006.05849/) (2020)
- [Development and Validation of a Deep Learning Algorithm for Improving Gleason Scoring of Prostate Cancer](https://mlsanity.com/paper/1811.06497/) (2018)
- [Deep Self-Taught Learning for Weakly Supervised Object Localization](https://mlsanity.com/paper/1704.05188/) (2017)
- [Self Paced Deep Learning for Weakly Supervised Object Detection](https://mlsanity.com/paper/1605.07651/) (2016)
- [Self-taught Object Localization with Deep Networks](https://mlsanity.com/paper/1409.3964/) (2014)

## Built on by (most cited first, 14 of 14)

- [Towards a Visual-Language Foundation Model for Computational Pathology](https://mlsanity.com/paper/2307.12914/) (2023)
- [CPLIP: Zero-Shot Learning for Histopathology with Comprehensive Vision-Language Alignment](https://mlsanity.com/paper/2406.05205/) (2024)
- [Sm: enhanced localization in Multiple Instance Learning for medical imaging classification](https://mlsanity.com/paper/2410.03276/) (2024)
- [Weakly-Supervised Deep Learning Model for Prostate Cancer Diagnosis and Gleason Grading of Histopathology Images](https://mlsanity.com/paper/2212.12844/) (2022)
- [Probabilistic Attention based on Gaussian Processes for Deep Multiple Instance Learning](https://mlsanity.com/paper/2302.04061/) (2023)
- [Multi-Resolution Pathology-Language Pre-training Model with Text-Guided Visual Representation](https://mlsanity.com/paper/2504.18856/) (2025)
- [A Novel Self-Learning Framework for Bladder Cancer Grading Using Histopathological Images](https://mlsanity.com/paper/2106.13559/) (2021)
- [Introducing instance label correlation in multiple instance learning. Application to cancer detection on histopathological images](https://mlsanity.com/paper/2310.19359/) (2023)
- [DeepGleason: a System for Automated Gleason Grading of Prostate Cancer using Deep Neural Networks](https://mlsanity.com/paper/2403.16678/) (2024)
- [Efficient Self-Supervised Grading of Prostate Cancer Pathology](https://mlsanity.com/paper/2501.15520/) (2025)
- [A self-training framework for glaucoma grading in OCT B-scans](https://mlsanity.com/paper/2111.11771/) (2021)
- [RACR-MIL: Rank-aware contextual reasoning for weakly supervised grading of squamous cell carcinoma using whole slide images](https://mlsanity.com/paper/2308.15618/) (2023)
- [Adaptive Prototype-based Interpretable Grading of Prostate Cancer](https://mlsanity.com/paper/2603.04947/) (2026)
- [From Multi-Resolution Cells to Gigapixel Whole Slide Images Foundation Model for Computational Pathology](https://mlsanity.com/paper/2608.03508/) (2026)

## Abstract (arXiv)

Prostate cancer is one of the main diseases affecting men worldwide. The gold standard for diagnosis and prognosis is the Gleason grading system. In this process, pathologists manually analyze prostate histology slides under microscope, in a high time-consuming and subjective task. In the last years, computer-aided-diagnosis (CAD) systems have emerged as a promising tool that could support pathologists in the daily clinical practice. Nevertheless, these systems are usually trained using tedious and prone-to-error pixel-level annotations of Gleason grades in the tissue. To alleviate the need of manual pixel-wise labeling, just a handful of works have been presented in the literature. Motivated by this, we propose a novel weakly-supervised deep-learning model, based on self-learning CNNs, that leverages only the global Gleason score of gigapixel whole slide images during training to accurately perform both, grading of patch-level patterns and biopsy-level scoring. To evaluate the performance of the proposed method, we perform extensive experiments on three different external datasets for the patch-level Gleason grading, and on two different test sets for global Grade Group prediction. We empirically demonstrate that our approach outperforms its supervised counterpart on patch-level Gleason grading by a large margin, as well as state-of-the-art methods on global biopsy-level scoring. Particularly, the proposed model brings an average improvement on the Cohen's quadratic kappa (k) score of nearly 18% compared to full-supervision for the patch-level Gleason grading task.