Domain Adaptive Classification for Compensating Variability in Histopathological Whole Slide Images

Histopathological whole slide images of the same organ stained with the same dye exhibit substantial inter-slide variation due to the manual preparation and staining process as well as due to inter-individual variability. In order to improve the generalization ability of a classification model on data from kidney pathology, we investigate a domain adaptation approach where a classifier trained on data from the source domain is presented a small number of userlabeled samples from the target domain. Domain adaptation resulted in improved classification performance, especially when combined with an interactive labeling procedure.