  Explainability of machine learning (ML) techniques in digital pathology (DP)
is of great significance to facilitate their wide adoption in clinics.
Recently, graph techniques encoding relevant biological entities have been
employed to represent and assess DP images. Such paradigm shift from pixel-wise
to entity-wise analysis provides more control over concept representation. In
this paper, we introduce a post-hoc explainer to derive compact per-instance
explanations emphasizing diagnostically important entities in the graph.
Although we focus our analyses to cells and cellular interactions in breast
cancer subtyping, the proposed explainer is generic enough to be extended to
other topological representations in DP. Qualitative and quantitative analyses
demonstrate the efficacy of the explainer in generating comprehensive and
compact explanations.
