What question did this study set out to answer?

The aim is to develop and assess weakly supervised deep learning methods for prostate cancer diagnosis and grading using whole slide images.

March 4, 2026Open Access

Benchmarking multiple instance learning architectures from patches to pathology for prostate cancer detection and grading using attention-based weak supervision

Key Points

The aim is to develop and assess weakly supervised deep learning methods for prostate cancer diagnosis and grading using whole slide images.
Benchmarking six multiple instance learning architectures and three feature encoders
Utilization of over 31 million tissue patches from the PANDA dataset
Implementation of attention mechanisms and Grad-CAM for interpretability
Distributed cloud computing for processing large volumes of data
Optimum configuration achieved 78.75% accuracy and 90.12% quadratic weighted kappa
Outperformed traditional methods and approached expert pathologist-level performance
Overlapping smaller patches enhanced spatial resolution and contextual understanding
Domain-specific models outperformed generic encoders

Abstract

Histopathological evaluation is necessary for the diagnosis and grading of prostate cancer, which is still one of the most common cancers in men globally. Traditional evaluation is time-consuming, prone to inter-observer variability, and challenging to scale. The clinical usefulness of current AI systems is limited by the need for comprehensive pixel-level annotations. The objective of this research is to develop and evaluate a large-scale benchmarking study on a weakly supervised deep learning framework that minimizes the need for annotation and ensures interpretability for automated prostate cancer diagnosis and International Society of Urological Pathology (ISUP) grading using whole slide images (WSIs). This study rigorously tested six cutting-edge multiple instance learning (MIL) architectures (CLAM-MB, CLAM-SB, ILRA-MIL, AC-MIL, AMD-MIL, WiKG-MIL), three feature encoders (ResNet50, CTransPath, UNI2), and four patch extraction techniques (varying sizes and overlap) using the PANDA dataset (10, 616 WSIs), yielding 72 experimental configurations. The methodology used distributed cloud computing to process over 31 million tissue patches, implementing advanced attention mechanisms to ensure clinical interpretability through Grad-CAM visualizations. The optimum configuration (UNI2 encoder with ILRA-MIL, 256256 patches, 50% overlap) achieved 78. 75% accuracy and 90. 12% quadratic weighted kappa (QWK), outperforming traditional methods and approaching expert pathologist-level diagnostic capability. Overlapping smaller patches offered the best balance of spatial resolution and contextual information, while domain-specific foundation models performed noticeably better than generic encoders. This work is the first large-scale, comprehensive comparison of weekly supervised MIL methods for prostate cancer diagnosis and grading. The proposed approach has excellent clinical diagnostic performance, scalability, practical feasibility through cloud computing, and interpretability using visualization tools.

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