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

• URL: http://arxiv.org/abs/2211.16141v1
• Date: Tue, 29 Nov 2022 12:16:39 GMT
• Title: Mind the Gap: Scanner-induced domain shifts pose challenges for representation learning in histopathology
• Authors: Frauke Wilm, Marco Fragoso, Christof A. Bertram, Nikolas Stathonikos, Mathias \"Ottl, Jingna Qiu, Robert Klopfleisch, Andreas Maier, Marc Aubreville, Katharina Breininger
• Abstract summary: Self-supervised pre-training can be used to overcome scanner-induced domain shifts for the downstream task of tumor segmentation. We show that self-supervised pre-training successfully aligned different scanner representations, which, interestingly only results in a limited benefit for our downstream task.
• Score: 6.309771474997404
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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