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Digital pathology is not only one of the most promising fields of diagnosticmedicine, but at the same time a hot topic for fundamental research. Digitalpathology is not just the transfer of histopathological slides into digitalrepresentations. The combination of different data sources (images, patientrecords, and *omics data) together with current advances in artificialintelligence/machine learning enable to make novel information accessible andquantifiable to a human expert, which is not yet available and not exploited incurrent medical settings. The grand goal is to reach a level of usableintelligence to understand the data in the context of an application task,thereby making machine decisions transparent, interpretable and explainable.The foundation of such an "augmented pathologist" needs an integrated approach:While machine learning algorithms require many thousands of training examples,a human expert is often confronted with only a few data points. Interestingly,humans can learn from such few examples and are able to instantly interpretcomplex patterns. Consequently, the grand goal is to combine the possibilitiesof artificial intelligence with human intelligence and to find a well-suitedbalance between them to enable what neither of them could do on their own. Thiscan raise the quality of education, diagnosis, prognosis and prediction ofcancer and other diseases. In this paper we describe some (incomplete) researchissues which we believe should be addressed in an integrated and concertedeffort for paving the way towards the augmented pathologist.

Towards the Augmented Pathologist: Challenges of Explainable-AI in Digital Pathology