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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.25175 |
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| _version_ | 1866911714545500160 |
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| author | Vardi, Ben Schonberger, Dana Friedmann, Yuval Yakhini, Zohar Barshack, Iris Loebel, Alexander Shamir, Ariel |
| author_facet | Vardi, Ben Schonberger, Dana Friedmann, Yuval Yakhini, Zohar Barshack, Iris Loebel, Alexander Shamir, Ariel |
| contents | Pathology foundation models (PFMs) have advanced rapidly in recent years and support training classifiers for a range of histopathology tasks. However, their robustness across hospitals remains limited: performance often degrades when training a classifier on data from one hospital and evaluating it on another target hospital. We address this challenge by fine-tuning PFMs with a local maximum mean discrepancy (LMMD) objective that applies to two settings: domain adaptation, where unlabeled target-hospital data is available, and domain generalization, where target-hospital data is unavailable at all. Experiments at both the patch- and slide-level show consistent improvements across multiple PFMs and tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_25175 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Discrepancy Minimization Improves Cross-Hospital Robustness in Digital Pathology Vardi, Ben Schonberger, Dana Friedmann, Yuval Yakhini, Zohar Barshack, Iris Loebel, Alexander Shamir, Ariel Computer Vision and Pattern Recognition Pathology foundation models (PFMs) have advanced rapidly in recent years and support training classifiers for a range of histopathology tasks. However, their robustness across hospitals remains limited: performance often degrades when training a classifier on data from one hospital and evaluating it on another target hospital. We address this challenge by fine-tuning PFMs with a local maximum mean discrepancy (LMMD) objective that applies to two settings: domain adaptation, where unlabeled target-hospital data is available, and domain generalization, where target-hospital data is unavailable at all. Experiments at both the patch- and slide-level show consistent improvements across multiple PFMs and tasks. |
| title | Discrepancy Minimization Improves Cross-Hospital Robustness in Digital Pathology |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.25175 |