Mind the Gap: Scanner-induced domain shifts pose challenges for representation learning in histopathology

Frauke Wilm; Marco Fragoso; Christof A. Bertram; Nikolas Stathonikos; Mathias Öttl; Jingna Qiu; Robert Klopfleisch; Andreas Maier; Marc Aubreville; Katharina Breininger

arXiv · 2211.16141

Mind the Gap: Scanner-induced domain shifts pose challenges for representation learning in histopathology

Frauke Wilm·Marco Fragoso·Christof A. Bertram·Nikolas Stathonikos·Mathias Öttl·Jingna Qiu·Robert Klopfleisch·Andreas Maier·Marc Aubreville·Katharina Breininger

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Abstract

Computer-aided systems in histopathology are often challenged by various sources of domain shift that impact the performance of these algorithms considerably. We investigated the potential of using self-supervised pre-training to overcome scanner-induced domain shifts for the downstream task of tumor segmentation. For this, we present the Barlow Triplets to learn scanner-invariant representations from a multi-scanner dataset with local image correspondences. We show that self-supervised pre-training successfully aligned different scanner representations, which, interestingly only results in a limited benefit for our downstream task. We thereby provide insights into the influence of scanner characteristics for downstream applications and contribute to a better understanding of why established self-supervised methods have not yet shown the same success on histopathology data as they have for natural images.

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Frauke Wilm, Marco Fragoso, Christof A. Bertram, Nikolas Stathonikos, Mathias Öttl, Jingna Qiu, Robert Klopfleisch, Andreas Maier, Marc Aubreville, Katharina Breininger. 2022-11-29. Mind the Gap: Scanner-induced domain shifts pose challenges for representation learning in histopathology. https://arxiv.org/abs/2211.16141

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