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Related Experiment Video

Updated: Jan 9, 2026

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

Beyond accuracy: Quantifying the reliability of multiple instance learning for whole slide image classification.

Hassan Keshvarikhojasteh¹, Marc Aubreville², Christof A Bertram³

¹Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.

|December 5, 2025

Summary

This summary is machine-generated.

Reliability of machine learning models in pathology is crucial. The mean pooling instance (MEAN-POOL-INS) model shows superior reliability for Whole Slide Image classification, offering a trustworthy baseline.

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Area of Science:

Computational pathology
Machine learning
Medical imaging analysis

Background:

Machine learning (ML) models are widely used but their reliability is a concern.
Multiple Instance Learning (MIL) models for Whole Slide Image (WSI) classification lack reliability evaluations.
This gap hinders their use in clinical decision-making.

Purpose of the Study:

To introduce quantitative metrics for assessing the reliability of MIL models.
To evaluate the reliability of common MIL architectures on pathology datasets.
To identify reliable MIL models for WSI classification.

Main Methods:

Developed three novel quantitative metrics for reliability assessment.
Applied metrics to several MIL architectures (e.g., MEAN-POOL-INS).
Utilized three region-wise annotated pathology datasets for evaluation.

Main Results:

The mean pooling instance (MEAN-POOL-INS) model exhibited superior reliability.
MEAN-POOL-INS demonstrated high reliability despite simple design and efficiency.
Reliability varied across different MIL architectures and datasets.

Conclusions:

Reliability assessment is essential for MIL models in computational pathology.
MEAN-POOL-INS serves as a reliable and efficient baseline for WSI classification.
Findings support the clinical applicability of trustworthy MIL models.