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Research PaperResearchia:202610.09049

One-Slide Calibration of Pathology Foundation Models

Ming Ren Hou

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% r...

Submitted: October 9, 2026Subjects: Medicine; Medical AI

Description / Details

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.


Source: arXiv:2610.08944v1 - http://arxiv.org/abs/2610.08944v1 PDF: https://arxiv.org/pdf/2610.08944v1 Original Link: http://arxiv.org/abs/2610.08944v1

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Submission Info
Date:
Oct 9, 2026
Topic:
Medical AI
Area:
Medicine
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