Assessment of Scanner Domain Shifts in Deep Multiple Instance Learning
Ganz J, Puget C, Ammeling J, Parlak E, Kiupel M, Bertram CA, Breininger K, Klopfleisch R, Aubreville M (2024)
Publication Type: Conference contribution
Publication year: 2024
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Pages Range: 137-142
Conference Proceedings Title: Informatik aktuell
Event location: Erlangen, DEU
ISBN: 9783658440367
DOI: 10.1007/978-3-658-44037-4_41
Abstract
Deep multiple instance learning is a popular method for classifying whole slide images, but it remains unclear how robust such models are against scanner-induced domain shifts. In this work, we studied this problem based on the classification of the mutational status of the c-Kit gene from whole slide images of canine mast cell tumors obtained with three different scanners. Furthermore, we investigated the possibility of utilizing image augmentation during feature extraction to overcome domain shifts. Our findings suggest that a notable domain shift exists between models trained on different scanners. Nevertheless, the use of image augmentations during feature extraction failed to address this domain shift and had no positive effect on in-domain performance.
Authors with CRIS profile
Involved external institutions
Technische Hochschule Ingolstadt
Germany (DE)
Freie Universität Berlin
Germany (DE)
Veterinärmedizinische Universität Wien (Vetmeduni Vienna) / University of Veterinary Medicine, Vienna
Austria (AT)
Michigan State University
United States (USA) (US)
Germany (DE)
Freie Universität Berlin
Germany (DE)
Veterinärmedizinische Universität Wien (Vetmeduni Vienna) / University of Veterinary Medicine, Vienna
Austria (AT)
Michigan State University
United States (USA) (US)
How to cite
APA:
Ganz, J., Puget, C., Ammeling, J., Parlak, E., Kiupel, M., Bertram, C.A.,... Aubreville, M. (2024). Assessment of Scanner Domain Shifts in Deep Multiple Instance Learning. In Andreas Maier, Thomas M. Deserno, Heinz Handels, Klaus Maier-Hein, Christoph Palm, Thomas Tolxdorff (Eds.), Informatik aktuell (pp. 137-142). Erlangen, DEU: Springer Science and Business Media Deutschland GmbH.
MLA:
Ganz, Jonathan, et al. "Assessment of Scanner Domain Shifts in Deep Multiple Instance Learning." Proceedings of the German Conference on Medical Image Computing, BVM 2024, Erlangen, DEU Ed. Andreas Maier, Thomas M. Deserno, Heinz Handels, Klaus Maier-Hein, Christoph Palm, Thomas Tolxdorff, Springer Science and Business Media Deutschland GmbH, 2024. 137-142.
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