\section{Implementation Details} \label{sec:appendixA}  
For ResGAT model, we trained for 30 epochs using Adam optimizer with learning rate $3 \times 10^{-4}$ and weight decay $1 \times 10^{-4}$. To account for randomness, each experiment was repeated with two random seeds 3 and 3407; the best-performing run is reported. Following standard MIL practice, we applied batch size of 1. For baseline methods, we used their recommended hyperparameters from official implementations to ensure fair comparison.

\section{Dataset Table} \label{sec:appendixB}

\input{sec/tables/table3}

\clearpage
\section{Supplementary Results} \label{sec:appendixC}
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\input{sec/tables/graph_ablation_all}
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\input{sec/tables/comp_efficiency}
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\begin{figure}[p]
\centering
\includegraphics[width=1.0\textwidth]{sec/figure/hyperparam_heatmap_2x2.pdf}
\caption{Hyperparameter sensitivity heatmaps across four datasets (Appendiceal Cancer, BRACS, TCGA-NSCLC, TCGA-ESCA). For each dataset, we visualize the performance over the hyperparameters
($d_{spa}$, $d_{feat}$) grid at two graph sparsity settings ($k=6,8$). The top row reports the primary metric (Balanced Accuracy for Appendiceal Cancer and BRACS datasets; Accuracy for others), and the bottom row reports AUC score. Within each dataset, the two heatmaps in the same row share the colorbar to enable direct comparison between $k$ values; brighter colors indicate better performance.}
\label{fig:hyperparam}
\end{figure}
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\begin{figure}[ht]
\centering
\includegraphics[width=0.85\linewidth]{sec/heatmap/WF53_thr0.70_ROI_comb.png}\\[3pt]
\includegraphics[width=0.85\linewidth]{sec/heatmap/S22_thr0.70_ROI_comb.png}\\[3pt]
\includegraphics[width=0.85\linewidth]{sec/heatmap/S36_thr0.70_ROI_comb.png}
\caption{Heatmap visualizations for representative MAC cases (WF53, S22, S36). The first row shows the aggregated heatmap and the corresponding high-contribution regions outlined in yellow, computed as a confidence-weighted average of Models 1, 2, and 5. The second row displays heatmaps from the five cross-validation models. Selected high-contribution patches are shown for localized inspection.}
\label{fig:2representatives}
\end{figure}
