\section{Conclusion}

In this work, we propose ResGAT, a graph-based MIL framework for WSI subtype classification. The architecture features a dual-branch residual graph attention design that preserves patch-specific features while adaptively aggregating graph-based context, helping mitigate the feature homogenization commonly associated with standard message passing. Our ablation study further shows that this dual-branch structure is effective, with direct patch-level feature propagation meaningfully complementing graph aggregation. Additionally, this study reveals that the proposed hybrid kNN graph topology, together with GraphNorm and a 3-layer GAT configuration, contributes to the overall performance of ResGAT. Our main results demonstrate that ResGAT outperforms SOTA MIL baselines on the class-imbalanced, label-noisy appendiceal cancer cohort and the challenging multi-class BRACS dataset, while remaining competitive on TCGA-NSCLC and TCGA-ESCA datasets. To assess model robustness under realistic deployment conditions, we introduce a cross-site evaluation protocol on the appendiceal cancer cohort that measures zero-shot generalization and few-shot adaptation across acquisition sites. In this setting, ResGAT reaches full target-site accuracy with only a few labeled slides and without forgetting the source domain. Notably, several MIL methods that perform well in general classification task fail to adapt under the cross-site setting. These observations suggest that, while general benchmarking provides a valuable baseline, extending evaluations to realistic diagnostic settings gives a more complete picture of a model's clinical efficacy.
