We presented a WSI analysis framework that bridges high-performance deep learning with clinical interpretability. By shifting from rigid patches to adaptive, biologically-grounded graphs, we preserve essential tissue structure while reducing computational cost. Enriching nodes with domain-specific nuclear and texture features allows predictions to be traced back to valid morphological evidence, aligning AI explanations with diagnostic workflows.

Empirically, our method outperforms comparable baselines and rivals the massive UNI2-h foundation model, demonstrating that resource-efficient, interpretable designs can compete with large-scale pre-training.

\textbf{Limitations and future work.} Future iterations will address the computational overhead of the nuclei detection preprocessing. We also plan to validate the clinical utility of our explanations through expert reader studies, explore extensions to other staining variations, and leverage the graph structure to integrate multi-modal genomic data.