DARE: Dual-Level Adversarial Learning with Domain-Aware Regularization for Whole Slide Image Classification

Pathological whole slide images (WSIs) from different hospitals often exhibit severe domain shifts due to variations in scanning devices, staining protocols, and tissue preparation procedures. This leads to significant performance degradation when a trained model is applied to data from different sources. Although unsupervised domain adaptation (UDA) has achieved strong performance in transferring a model trained on a source domain to an unlabeled target domain in natural images, it is much less studied in the computational pathology field. This is due to the unique properties of WSIs, including ultra-high resolution and strong inter-slide and intra-slide heterogeneity. As a result, existing UDA methods are often unstable and may even cause negative transfer in WSI classification. To address this issue, we propose Dual-level Adversarial learning with domain-aware REgularization (DARE) for WSI Classification. Specifically, we design an adaptive pseudo-labeling framework with a teacher-student architecture to provide stable supervision for the target domain and perform adversarial alignment on both patch-level and slide-level embeddings. This enables joint optimization of patch-level and slide-level semantic embeddings for dual-level alignment across domains. We also introduce domain-aware attention masking to regularize attention and improve cross-domain generalization. We evaluate our method on four public WSI datasets through extensive experiments. The results show that our method consistently outperforms state-of-the-art approaches across various domain shifts.