  Instance-level graph neural network explainers have proven beneficial for
explaining such networks on histopathology images. However, there has been few
methods that provide model explanations, which are common patterns among
samples within the same class. We envision that graph-based histopathological
image analysis can benefit significantly from such explanations. On the other
hand, current model-level explainers are based on graph generation methods that
are not applicable in this domain because of no corresponding image for their
generated graphs in real world. Therefore, such explanations are communicable
to the experts. To follow this vision, we developed KS-GNNExplainer, the first
instance-level graph neural network explainer that leverages current
instance-level approaches in an effective manner to provide more informative
and reliable explainable outputs, which are crucial for applied AI in the
health domain. Our experiments on various datasets, and based on both
quantitative and qualitative measures, demonstrate that the proposed explainer
is capable of being a global pattern extractor, which is a fundamental
limitation of current instance-level approaches in this domain.
