Navigating the MIL Trade-Off: Flexible Pooling for Whole Slide Image Classification
TL;DR
Maxsoft is introduced—a novel MIL pooling function that enables flexible control over this trade-off, allowing adaptation to specific tasks and datasets, and PerPatch augmentation is proposed—a simple yet effective technique that enhances model robustness.
Abstract
Multiple Instance Learning (MIL) is a standard weakly supervised approach for Whole Slide Image (WSI) classification, where performance hinges on both feature representation and MIL pooling strategies. Recent research has predominantly focused on Transformer-based architectures adapted for WSIs. However, we argue that this trend faces a fundamental limitation: data scarcity. In typical settings, Transformer models yield only marginal gains without access to large-scale datasets—resources that are virtually inaccessible to all but a few well-funded research labs. Motivated by this, we revisit simple, non-attention MIL with unsupervised slide features and analyze temperature-β -controlled log-sum-exp (LSE) pooling. For slides partitioned into N patches, we theoretically show that LSE has a smooth transition at a critical β crit = O (log N ) threshold, interpolating between mean-like aggregation (stable, better generalization but less sensitive) and max-like aggregation (more sensitive but looser generalization bounds). Grounded in this analysis, we introduce Maxsoft—a novel MIL pooling function that enables flexible control over this trade-off, allowing adaptation to specific tasks and datasets. To further tackle real-world deployment challenges such as specimen heterogeneity, we propose PerPatch augmentation—a simple yet effective technique that enhances model robustness