Weakly Supervised Instance-Level Gleason Pattern Estimation Using Primary and Secondary Labels

• URL: http://arxiv.org/abs/2607.23594v2
• Date: Sun, 02 Aug 2026 11:30:19 GMT
• Title: Weakly Supervised Instance-Level Gleason Pattern Estimation Using Primary and Secondary Labels
• Abstract summary: In prostate cancer histopathology, the Gleason Score is determined by the most frequent (Primary) and second most frequent (Secondary) Gleason patterns within a whole-slide image.<n>We propose a Multiple Instance Learning framework that estimates instance-level Gleason patterns from slide-level Primary and Secondary labels.
• Score: 8.166364251367625
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In prostate cancer histopathology, the Gleason Score is determined by the most frequent (Primary) and second most frequent (Secondary) Gleason patterns within a whole-slide image. Although these slide-level labels are routinely available in clinical practice, instance-level Gleason annotations are rarely provided, making patch-level learning challenging. We propose a Multiple Instance Learning (MIL) framework that estimates instance-level Gleason patterns from slide-level Primary and Secondary labels. The proposed method formulates instance-level learning according to the clinical definition of the Gleason Score by aggregating instance predictions into class counts and explicitly modeling the Primary pattern, Secondary pattern, and their dominance. Experimental results demonstrate that the proposed formulation enables effective instance-level learning and outperforms existing MIL approaches on the SICAP-MIL dataset.

Related papers

• Clinical Cognition Alignment for Gastrointestinal Diagnosis with Multimodal LLMs [63.535652574541764]
  Multimodal Large Language Models (MLLMs) have demonstrated remarkable potential in medical image analysis.<n>Their application in gastrointestinal endoscopy is currently hindered by two critical limitations.<n>We propose a novel Clinical-Cognitive-Aligned (CogAlign) framework to address these challenges.
  arXiv  Detail & Related papers  (2026-03-21T07:47:37Z)
• Learning Consistent Taxonomic Classification through Hierarchical Reasoning [61.372270953201955]
  We propose a two-stage, hierarchy-based reasoning framework designed to improve leaf-level accuracy and hierarchical consistency in taxonomic classification.<n>Our framework, implemented on the Qwen2.5-VL-7B model, outperforms its original 72B counterpart by over 10% in both leaf-level and hierarchical consistency accuracy.
  arXiv  Detail & Related papers  (2026-01-21T03:00:00Z)
• Efficient Self-Supervised Grading of Prostate Cancer Pathology [0.06554326244334867]
  The proposed framework is based on a Task-specific Self-supervised learning model, which is fine-tuned using Ordinal Regression.<n>The model is then fine-tuned for the task of ISUP grading using an ordinal regression-based approach.
  arXiv  Detail & Related papers  (2025-01-26T13:22:04Z)
• Position: From Correlation to Causation: Max-Pooling-Based Multi-Instance Learning Leads to More Robust Whole Slide Image Classification [51.95824566163554]
  We argue that well-trained max-pooling-based MIL models can make predictions based on causal factors and avoid relying on spurious correlations.<n>We propose a simple yet effective max-pooling-based MIL method (FocusMIL) that outperforms existing mainstream attention-based methods on two datasets.
  arXiv  Detail & Related papers  (2024-08-18T12:15:22Z)
• Poisson Ordinal Network for Gleason Group Estimation Using Bi-Parametric MRI [15.754944195515504]
  Gleason groups serve as the primary histological grading system for prostate cancer. In clinical practice, pathologists determine the Gleason groups based on specimens obtained from ultrasound-guided biopsies. We investigate the feasibility of directly estimating the Gleason groups from MRI scans to reduce otherwise required biopsies.
  arXiv  Detail & Related papers  (2024-07-08T09:56:30Z)
• Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective [51.70661197256033]
  We propose ARCO, a semi-supervised contrastive learning framework with stratified group theory for medical image segmentation. We first propose building ARCO through the concept of variance-reduced estimation and show that certain variance-reduction techniques are particularly beneficial in pixel/voxel-level segmentation tasks. We experimentally validate our approaches on eight benchmarks, i.e., five 2D/3D medical and three semantic segmentation datasets, with different label settings.
  arXiv  Detail & Related papers  (2023-02-03T13:50:25Z)
• Parametric Classification for Generalized Category Discovery: A Baseline Study [70.73212959385387]
  Generalized Category Discovery (GCD) aims to discover novel categories in unlabelled datasets using knowledge learned from labelled samples. We investigate the failure of parametric classifiers, verify the effectiveness of previous design choices when high-quality supervision is available, and identify unreliable pseudo-labels as a key problem. We propose a simple yet effective parametric classification method that benefits from entropy regularisation, achieves state-of-the-art performance on multiple GCD benchmarks and shows strong robustness to unknown class numbers.
  arXiv  Detail & Related papers  (2022-11-21T18:47:11Z)
• Multimodal Generalized Zero Shot Learning for Gleason Grading using Self-Supervised Learning [4.898744396854313]
  Gleason grading from histopathology images is essential for accurate prostate cancer diagnosis. We propose a method to predict Gleason grades from magnetic resonance (MR) images which are non-interventional and easily acquired.
  arXiv  Detail & Related papers  (2021-11-15T10:14:11Z)
• ECKPN: Explicit Class Knowledge Propagation Network for Transductive Few-shot Learning [53.09923823663554]
  Class-level knowledge can be easily learned by humans from just a handful of samples. We propose an Explicit Class Knowledge Propagation Network (ECKPN) to address this problem. We conduct extensive experiments on four few-shot classification benchmarks, and the experimental results show that the proposed ECKPN significantly outperforms the state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-06-16T02:29:43Z)
• WeGleNet: A Weakly-Supervised Convolutional Neural Network for the Semantic Segmentation of Gleason Grades in Prostate Histology Images [1.52819437883813]
  We propose a deep-learning-based system able to detect local cancerous patterns in the prostate tissue using only the global-level Gleason score during training. We obtained a Cohen's quadratic kappa (k) of 0.67 for the pixel-level prediction of cancerous patterns in the validation cohort. We compared the model performance for semantic segmentation of Gleason grades with supervised state-of-the-art architectures in the test cohort.
  arXiv  Detail & Related papers  (2021-05-21T16:27:16Z)
• Self-learning for weakly supervised Gleason grading of local patterns [6.97280833203187]
  We propose a weakly-supervised deep-learning model, based on self-learning CNNs, to accurately perform both, grading of patch-level patterns and biopsy-level scoring. We empirically demonstrate that our approach outperforms its supervised counterpart on patch-level Gleason grading by a large margin.
  arXiv  Detail & Related papers  (2021-05-21T15:39:50Z)
• The Devil is in Classification: A Simple Framework for Long-tail Object Detection and Instance Segmentation [93.17367076148348]
  We investigate performance drop of the state-of-the-art two-stage instance segmentation model Mask R-CNN on the recent long-tail LVIS dataset. We unveil that a major cause is the inaccurate classification of object proposals. We propose a simple calibration framework to more effectively alleviate classification head bias with a bi-level class balanced sampling approach.
  arXiv  Detail & Related papers  (2020-07-23T12:49:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.