SMART: Preserving Diagnostic Semantics for Unsupervised Domain Adaptation in Whole Slide Image Classification

While pre-trained foundation models and Multiple Instance Learning (MIL) have advanced computational pathology, their clinical deployment remains severely hindered by cross-institutional domain shifts. Although Unsupervised Domain Adaptation (UDA) offers a practical solution to such discrepancies, directly applying general UDA paradigms to gigapixel Whole Slide Images (WSIs) often leads to negative transfer. This is because aggregated slide-level representations inherently entangle domain-specific confounders with task-relevant diagnostic semantics, exacerbating pseudo-label confirmation bias in self-training and marginalizing hard target cases under asymmetric domain shifts. To address these challenges, we propose Semantic Memory-Augmented Robust Transfer (SMART), a novel UDA framework that jointly optimizes across feature, label, and distribution dimensions. At the feature dimension, a convergence-aware variational information bottleneck dynamically purifies representations by filtering domain-specific confounders while preserving diagnostic semantics. Leveraging this purified latent space, we mitigate self-training confirmation bias at the label dimension by retrieving and incorporating reliable non-parametric source priors. Finally, at the distribution dimension, we address asymmetric domain shifts through an entropy-guided cross-difficulty mixup strategy. Extensive evaluations on real-world cohorts demonstrate consistent improvements over state-of-the-art methods. Designed as a plug-and-play framework, SMART can be readily integrated into existing bag-based MIL methods, substantially improving cross-institutional WSI classification. Code will be released.