  Explainability of deep learning methods is imperative to facilitate their
clinical adoption in digital pathology. However, popular deep learning methods
and explainability techniques (explainers) based on pixel-wise processing
disregard biological entities' notion, thus complicating comprehension by
pathologists. In this work, we address this by adopting biological entity-based
graph processing and graph explainers enabling explanations accessible to
pathologists. In this context, a major challenge becomes to discern meaningful
explainers, particularly in a standardized and quantifiable fashion. To this
end, we propose herein a set of novel quantitative metrics based on statistics
of class separability using pathologically measurable concepts to characterize
graph explainers. We employ the proposed metrics to evaluate three types of
graph explainers, namely the layer-wise relevance propagation, gradient-based
saliency, and graph pruning approaches, to explain Cell-Graph representations
for Breast Cancer Subtyping. The proposed metrics are also applicable in other
domains by using domain-specific intuitive concepts. We validate the
qualitative and quantitative findings on the BRACS dataset, a large cohort of
breast cancer RoIs, by expert pathologists.
