Survey of XAI in digital pathology

TL;DR

This survey reviews explainable AI techniques in digital pathology, highlighting their relevance, categorization, and integration with uncertainty estimation to guide future research and facilitate cross-disciplinary understanding.

Contribution

It provides a comprehensive overview of XAI methods in digital pathology, categorizes them, and connects technical approaches with clinical needs, fostering collaboration.

Findings

Categorized XAI techniques relevant for pathology imaging

Integrated uncertainty estimation into XAI landscape

Guided future research directions in digital pathology

Abstract

Artificial intelligence (AI) has shown great promise for diagnostic imaging assessments. However, the application of AI to support medical diagnostics in clinical routine comes with many challenges. The algorithms should have high prediction accuracy but also be transparent, understandable and reliable. Thus, explainable artificial intelligence (XAI) is highly relevant for this domain. We present a survey on XAI within digital pathology, a medical imaging sub-discipline with particular characteristics and needs. The review includes several contributions. Firstly, we give a thorough overview of current XAI techniques of potential relevance for deep learning methods in pathology imaging, and categorise them from three different aspects. In doing so, we incorporate uncertainty estimation methods as an integral part of the XAI landscape. We also connect the technical methods to the specific prerequisites in digital pathology and present findings to guide future research efforts. The survey is intended for both technical researchers and medical professionals, one of the objectives being to establish a common ground for cross-disciplinary discussions.