Quantifying Explainers of Graph Neural Networks in Computational Pathology

Understand

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.