  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.
