{"slug": "one-slide-calibration-of-pathology-foundation-models", "title": "One-Slide Calibration of Pathology Foundation Models", "summary": "A new method called SlideRuler calibrates pathology foundation models from a single slide scan, reducing mean target-to-source embedding distance by 16.3-38.5% relative to raw embeddings across two encoders and five SCORPION scanners, according to the arXiv paper 2610.08944v1. SlideRuler learns a transfer map from paired rescans and uses regions within a slide as internal controls to correct acquisition-induced shifts while keeping the foundation model frozen. A source-anchored variant cuts source-feature displacement by 47.7-83.6% relative to learned transfer while retaining most of its alignment gain, with positive same-slide contribution across all four evaluation settings, including scanner holdout.", "body_md": "arXiv:2610.08944v1 Announce Type: new \nAbstract: 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.", "url": "https://wpnews.pro/news/one-slide-calibration-of-pathology-foundation-models", "canonical_source": "https://arxiv.org/abs/2610.08944", "published_at": "2026-10-08 04:00:00+00:00", "updated_at": "2026-10-08 04:19:41.918413+00:00", "lang": "en", "topics": ["ai-research", "machine-learning", "computer-vision", "artificial-intelligence"], "entities": ["SlideRuler", "SCORPION", "arXiv"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/one-slide-calibration-of-pathology-foundation-models", "markdown": "https://wpnews.pro/news/one-slide-calibration-of-pathology-foundation-models.md", "text": "https://wpnews.pro/news/one-slide-calibration-of-pathology-foundation-models.txt", "jsonld": "https://wpnews.pro/news/one-slide-calibration-of-pathology-foundation-models.jsonld"}}