\begin{abstract}

Multiple instance learning (MIL) provides a weakly supervised framework for whole slide image (WSI) classification, enabling slide-level prediction from gigapixel images with only slide-level labels. However, WSI subtype classification in realistic settings is still challenging. In this work, we propose ResGAT, a residual graph attention framework that operates on hybrid $k$-NN patch graphs and models WSI representations with stacked residual graph attention blocks. ResGAT is evaluated on the subtype classification task across a rare, class-imbalanced appendiceal cancer cohort, BRACS and two TCGA datasets. It outperforms SOTA MIL baselines on the appendiceal cancer and BRACS cohorts, and remains competitive on the TCGA datasets. On the appendiceal cancer cohort, we further assess cross-site generalization via few-shot adaptation under source shift, showing that ResGAT adapts effectively to new domains with limited labels. An ablation study is provided to validate the effectiveness of key architectural components of our method.

\end{abstract}

\begin{keywords}
whole slide image classification, multiple instance learning, residual graph attention framework, cross-site generalization
\end{keywords}