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Survey of XAI in digital pathology

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Document pages: 36 pages

Abstract: Artificial intelligence (AI) has shown great promise for diagnostic imagingassessments. However, the application of AI to support medical diagnostics inclinical routine comes with many challenges. The algorithms should have highprediction 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 imagingsub-discipline with particular characteristics and needs. The review includesseveral contributions. Firstly, we give a thorough overview of current XAItechniques of potential relevance for deep learning methods in pathologyimaging, and categorise them from three different aspects. In doing so, weincorporate uncertainty estimation methods as an integral part of the XAIlandscape. We also connect the technical methods to the specific prerequisitesin digital pathology and present findings to guide future research efforts. Thesurvey is intended for both technical researchers and medical professionals,one of the objectives being to establish a common ground for cross-disciplinarydiscussions.

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