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Instance-level graph neural network explainers have proven beneficial forexplaining such networks on histopathology images. However, there has been fewmethods that provide model explanations, which are common patterns amongsamples within the same class. We envision that graph-based histopathologicalimage analysis can benefit significantly from such explanations. On the otherhand, current model-level explainers are based on graph generation methods thatare not applicable in this domain because of no corresponding image for theirgenerated graphs in real world. Therefore, such explanations are communicableto the experts. To follow this vision, we developed KS-GNNExplainer, the firstinstance-level graph neural network explainer that leverages currentinstance-level approaches in an effective manner to provide more informativeand reliable explainable outputs, which are crucial for applied AI in thehealth domain. Our experiments on various datasets, and based on bothquantitative and qualitative measures, demonstrate that the proposed explaineris capable of being a global pattern extractor, which is a fundamentallimitation of current instance-level approaches in this domain.

KS-GNNExplainer: Global Model Interpretation Through Instance Explanations On Histopathology images