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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

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Dual selective gleason pattern-aware multiple instance learning with uncertainty regularization for grade group

Xinyu Hao¹, Hongming Xu², Jingdong Zhang³

¹Cancer Hospital of Dalian University of Technology, Shenyang, 110042, China; School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian, 116024, China; School of Software Technology, Dalian University of Technology, Dalian, 116024, China; Faculty of Information Technology, University of Jyväskylä, Jyväskylä, 40014, Finland.

Medical Image Analysis

|February 28, 2026

Summary

This study introduces DSPA-U-MIL, a novel model for predicting prostate cancer Gleason Grade Group (GG). The method improves accuracy and interpretability by considering different Gleason Patterns, outperforming existing approaches.

Keywords:

Expert concepts Prostate cancer Selective aggregation Uncertainty estimation Whole slide image

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Area of Science:

Digital pathology
Computational oncology
Machine learning in medicine

Background:

Accurate Gleason Grade Group (GG) prediction is crucial for prostate cancer management.
Existing multiple instance learning (MIL) methods for Gleason grading often neglect the combined influence of different Gleason Patterns, impacting accuracy and interpretability.

Purpose of the Study:

To develop an uncertainty-driven, dual-selective, Gleason Pattern-aware MIL model (DSPA-U-MIL) for improved patient-level GG prediction.
To enhance the accuracy and interpretability of Gleason grading by incorporating domain knowledge of Gleason Patterns.

Main Methods:

Proposed DSPA-U-MIL model integrating learnable pattern aggregation tokens and expert concept-guided patch-level aggregation.
Employed a teacher-student knowledge distillation framework for cooperative prediction across Gleason Patterns.
Introduced an uncertainty constraint to improve robustness against noisy labels.

Main Results:

DSPA-U-MIL demonstrated superior performance compared to existing MIL methods across five datasets (10,809 WSIs, 1133 TMAs).
Achieved up to a 6.7% improvement in quadratic weighted kappa (QWK) score over the CLAM baseline, particularly on the TCGA-PRAD dataset.
Student model's gating weight distributions aligned with pathologist annotations, confirming interpretability.

Conclusions:

DSPA-U-MIL offers a significant advancement in automated Gleason GG prediction, enhancing both predictive accuracy and clinical interpretability.
The model's ability to leverage domain knowledge of Gleason Patterns and handle label noise makes it a valuable tool for prostate cancer risk stratification.