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 Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2024

Journal

Informatik aktuell Gesellschaft für Informatik (GI)

Publisher: Springer Vieweg

Series: Informatik aktuell

City/Town: Wiesbaden

Pages Range: 137-142

Conference Proceedings Title: Bildverarbeitung für die Medizin 2024. BVM 2024

Event location: Erlangen DE

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

Katharina Breininger Juniorprofessur für Artificial Intelligence in Medical Imaging

Involved external institutions

Technische Hochschule Ingolstadt DE Germany (DE) Freie Universität Berlin DE Germany (DE) Michigan State University US United States (USA) (US) Veterinärmedizinische Universität Wien (Vetmeduni Vienna) / University of Veterinary Medicine, Vienna AT Austria (AT)

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.), Bildverarbeitung für die Medizin 2024. BVM 2024 (pp. 137-142). Erlangen, DE: Wiesbaden: Springer Vieweg.

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 Ed. Andreas Maier, Thomas M. Deserno, Heinz Handels, Klaus Maier-Hein, Christoph Palm, Thomas Tolxdorff, Wiesbaden: Springer Vieweg, 2024. 137-142.

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