Mind the Gap: Scanner-induced domain shifts pose challenges for representation learning in histopathology
Fuente:
arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2022
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| Materias: | |
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| _version_ | 1866929559679533056 |
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| author | Wilm, Frauke Fragoso, Marco Bertram, Christof A. Stathonikos, Nikolas Öttl, Mathias Qiu, Jingna Klopfleisch, Robert Maier, Andreas Aubreville, Marc Breininger, Katharina |
| author_facet | Wilm, Frauke Fragoso, Marco Bertram, Christof A. Stathonikos, Nikolas Öttl, Mathias Qiu, Jingna Klopfleisch, Robert Maier, Andreas Aubreville, Marc Breininger, Katharina |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_16141 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Mind the Gap: Scanner-induced domain shifts pose challenges for representation learning in histopathology Wilm, Frauke Fragoso, Marco Bertram, Christof A. Stathonikos, Nikolas Öttl, Mathias Qiu, Jingna Klopfleisch, Robert Maier, Andreas Aubreville, Marc Breininger, Katharina Image and Video Processing Computer Vision and Pattern Recognition 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. |
| title | Mind the Gap: Scanner-induced domain shifts pose challenges for representation learning in histopathology |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2211.16141 |