# One-Slide Calibration

The authors introduce SlideRuler, which treats other regions of the same slide as internal controls and learns a transfer map from paired rescans to estimate and correct acquisition-induced shifts, enabling calibration from a single scan at inference while keeping the foundation model frozen; 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, same-slide controls outperform unrelated same-scanner controls, and a source-anchored variant reduces source-feature displacement by 47.7–83.6% while retaining most of its alignment gain.

The authors introduce SlideRuler, which treats other regions of the same slide as internal controls and learns a transfer map from paired rescans to estimate and correct acquisition-induced shifts, enabling calibration from a single scan at inference while keeping the foundation model frozen; 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, same-slide controls outperform unrelated same-scanner controls, and a source-anchored variant reduces source-feature displacement by 47.7–83.6% while retaining most of its alignment gain.