One-Slide Calibration of Pathology Foundation Models

arXiv:2610.08944 · cs.CV, cs.AI · Submitted 2026-10-06 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-10-06

Updated: 2026-10-21

Comments: Accepted at the NeurIPS 2026 Workshop AI at Scale for Clinical Impact (ASCI): Cancer Pathology Foundation Models

License: http://creativecommons.org/licenses/by-sa/4.0/

The gist: Scanner variation changes how pathology foundation models represent the same tissue.

Terminology

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.

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