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

ABSTRACT: Machine learning models have become integral to many fields, but their reliability, defined as producing dependable, trustworthy, and domain-consistent predictions, remains a critical concern. Multiple Instance Learning (MIL) models designed for Whole Slide Image (WSI) classification in computational pathology are rarely evaluated in terms of reliability, leaving a key gap in understanding their suitability for high-stakes applications like clinical decision-making. In this paper, we address this gap by introducing three quantitative metrics for reliability assessment and applying them to several widely used MIL architectures across three region-wise annotated pathology datasets. Our findings indicate that the mean pooling instance (MEAN-POOL-INS) model demonstrates superior reliability co

SUBMITTER: Keshvarikhojasteh H 

PROVIDER: S-EPMC12680235 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature