# Beyond accuracy: quantifying the reliability of Multiple Instance Learning for Whole Slide Image classification Source: [Beyond accuracy: quantifying the reliability of Multiple Instance Learning for Whole Slide Image classification]() Collection index: [llms.txt]() Data updated: 2025-12-11T05:02:08.227Z Authors: Hassan Keshvarikhojasteh, Marc Aubreville, Christof A. Bertram, Josien P.W. Pluim, Mitko Veta Published: 2024-09-17T12:04:18.000Z ## 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 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()