ASC: Learning Augmentation Severity-Consistent Representations Improves Generalization via Augmentation Search

Whole Slide Image (WSI) classification is hindered by limited data availability, resulting in weak generalization. Recent efforts leverage data augmentation to address this, but methods adapted from natural images often fail on WSIs—either degrading performance or offering marginal gains. A central challenge lies in tuning augmentation parameters to match WSI-specific characteristics, a task rendered impractical by the computational demands of current WSI pipelines, where feature extraction is frozen and prohibitively expensive. This work introduces two key contributions. First, it proposes DINOASC, an enhanced self-supervised learning framework that modifies DINO to produce embeddings with AugSev Consistency—a property ensuring that linear interpolations across augmentation severities yield semantically coherent representations. Second, it presents the first automatic augmentation search strategy for WSI classification, built on top of TrivialAugment, which efficiently discovers augmentation strength ranges suited to histopathology by exploiting the structured embedding space induced by DINOASC. Together, these components enable augmentation-based generalization improvements without incurring excessive computational overhead. The proposed method achieves state-of-the-art performance on CAMELYON16 and SICAP-MIL.