Abstract
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 user-labeled samples from the target domain. Domain adaptation resulted in improved classification performance, especially when combined with an interactive labeling procedure.
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Gadermayr, M., Strauch, M., Klinkhammer, B.M., Djudjaj, S., Boor, P., Merhof, D. (2016). Domain Adaptive Classification for Compensating Variability in Histopathological Whole Slide Images. In: Campilho, A., Karray, F. (eds) Image Analysis and Recognition. ICIAR 2016. Lecture Notes in Computer Science(), vol 9730. Springer, Cham. https://doi.org/10.1007/978-3-319-41501-7_69
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DOI: https://doi.org/10.1007/978-3-319-41501-7_69
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