Title: Towards the Augmented Pathologist: Challenges of Explainable-AI in Digital Pathology,
Abstract: Digital pathology is not only one of the most promising fields of diagnostic
medicine, but at the same time a hot topic for fundamental research. Digital
pathology is not just the transfer of histopathological slides into digital
representations. The combination of different data sources (images, patient
records, and *omics data) together with current advances in artificial
intelligence/machine learning enable to make novel information accessible and
quantifiable to a human expert, which is not yet available and not exploited in
current medical settings. The grand goal is to reach a level of usable
intelligence 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 interpret
complex patterns. Consequently, the grand goal is to combine the possibilities
of artificial intelligence with human intelligence and to find a well-suited
balance between them to enable what neither of them could do on their own. This
can raise the quality of education, diagnosis, prognosis and prediction of
cancer and other diseases. In this paper we describe some (incomplete) research
issues which we believe should be addressed in an integrated and concerted
effort for paving the way towards the augmented pathologist.