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.

本篇研究介绍了一种后处理解释器，可以从图形中提取关键诊断实体，从而进行细胞和细胞间的分类。该解释器具有广泛的应用前景，与数字病理的拓扑表示法相结合，可以产生更全面的解释性结果。