University of Veterinary Medicine Vienna Beyond accuracy: Quantifying the reliability of multiple instance learning for whole slide image classification 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 compared to other networks, despite its simple architectural design and computational efficiency. These findings underscore the need of reliability evaluation alongside predictive performance in MIL models and establish MEAN-POOL-INS as a strong, trustworthy baseline for future research. en TEXT journal article Wissenschaftlicher Artikel Articolo scientifico Text journal article Documento PDF Articolo scientifico Text Wissenschaftlicher Artikel Machine Learning Cancers And Neoplasms Sequence Alignment Breast Cancer Tissue Distribution Genome Annotation Reliability Learning Curves 2025 2025 Hassan Keshvarikhojasteh Marc Aubreville Christof A. Bertram Josien P. W. Pluim Mitko Veta Public Library of Science