[Submitted on 6 Oct 2026]
Title:One-Slide Calibration of Pathology Foundation Models
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Abstract:Scanner variation changes how pathology foundation models represent the same tissue. We introduce SlideRuler, which uses regions within a slide as internal controls to estimate and correct acquisition-induced shifts in other regions. A transfer map learned from paired rescans enables calibration from a single scan at inference while keeping the foundation model fixed. Across two encoders and five SCORPION scanners, learned transfer reduces mean target-to-source embedding distance by 16.3-38.5% relative to raw embeddings. Comparisons with unrelated same-scanner controls reveal a positive same-slide contribution across all four evaluation settings, including scanner holdout. A source-anchored variant reduces source-feature displacement by 47.7-83.6% relative to learned transfer while retaining most of its alignment gain. By drawing calibration information from the slide itself, SlideRuler offers a path toward more consistent use of frozen pathology models across imaging systems.
Comments: Accepted at the NeurIPS 2026 Workshop AI at Scale for Clinical Impact (ASCI): Cancer Pathology Foundation Models
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2610.08944 [cs.CV]
(or arXiv:2610.08944v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.08944
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Tianyi Huang [view email] [v1] Tue, 6 Oct 2026 18:09:36 UTC (2,184 KB)
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