Towards Explainable Graph Representations in Digital Pathology

Abstract

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.

ICML'20 workshop on Computational Biology

References in corpus (5)

• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Distilling the Knowledge in a Neural Network
• Neural Message Passing for Quantum Chemistry
• Towards the Augmented Pathologist: Challenges of Explainable-AI in Digital Pathology
• Weakly Supervised Prostate TMA Classification via Graph Convolutional Networks

Cited by in corpus (5)

• Graph Representation Learning in Biomedicine
• Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future
• A Survey on Graph-Based Deep Learning for Computational Histopathology
• Generative Causal Explanations for Graph Neural Networks
• Hierarchical Phenotyping and Graph Modeling of Spatial Architecture in Lymphoid Neoplasms