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arxiv logo>eess> arXiv:2211.16141
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Electrical Engineering and Systems Science > Image and Video Processing

arXiv:2211.16141 (eess)
[Submitted on 29 Nov 2022]

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

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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.
Comments:5 pages, 4 figures, 1 table. This work has been submitted to the IEEE for possible publication
Subjects:Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
Cite as:arXiv:2211.16141 [eess.IV]
 (orarXiv:2211.16141v1 [eess.IV] for this version)
 https://doi.org/10.48550/arXiv.2211.16141
arXiv-issued DOI via DataCite

Submission history

From: Frauke Wilm [view email]
[v1] Tue, 29 Nov 2022 12:16:39 UTC (1,093 KB)
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