Survey of XAI in digital pathology
This survey aims to bridge gaps between technical researchers and medical professionals in digital pathology, though it is incremental as it reviews existing methods without introducing new ones.
The authors conducted a survey on explainable artificial intelligence (XAI) in digital pathology, addressing the need for transparent and reliable AI in medical diagnostics by categorizing current XAI techniques and connecting them to domain-specific prerequisites.
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.