Whole Slide Image (WSI) classification is hindered by limited data availability, resulting in weak generalization. Recent efforts leverage image 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 frozen feature extraction of the tiles is necessary. 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, inspired by 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.

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-$\beta$-controlled log-sum-exp (LSE) pooling. For slides partitioned into $N$ patches, we theoretically show that LSE has a smooth transition at a critical $\beta_{\mathrm{crit}}=\mathcal{O}(\log N)$ 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. Empirically, Maxsoft achieves state-of-the-art performance in low-data regimes across four major benchmarks (CAMELYON16, CAMELYON17, TCGA-Lung, and SICAP-MIL), often matching or surpassing large-scale foundation models. When combined with PerPatch augmentation, this performance is further improved through increased robustness. Code is available at \href{https://github.com/jafarinia/maxsoft}{\texttt{https://github.com/jafarinia/maxsoft}}