arXiv cs.AI· Ming Ren Hou, Tianyi Huang·· 13 小时前AI 评分22
SlideRuler：用单张切片校准病理基础模型
One-Slide Calibration of Pathology Foundation Models
AI 导读
SlideRuler 利用同一张切片内的其他区域作为内部对照，预测查询区域的扫描仪偏移，仅凭单次扫描即可完成校准且保持基础模型冻结。在两个编码器和五台 SCORPION 扫描仪上，学习到的迁移映射将平均目标到源嵌入距离相对原始嵌入降低 16.3-38.5%。源锚定变体进一步将源特征位移相对学习迁移降低 47.7-83.6%，同时保留大部分对齐增益。
正文 · 原文
Abstract:Scanner variation changes how pathology foundation models represent the same tissue. We introduce SlideRuler, which uses other regions within a slide as internal controls to predict a query region's scanner-induced shift. 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. Replacing controls with unrelated same-scanner regions reveals a positive same-slide contribution in 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:2610.08944v2 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.08944 arXiv-issued DOI via DataCite |
Submission history
From: Tianyi Huang [view email]
[v1]
Tue, 6 Oct 2026 18:09:36 UTC (2,184 KB)
[v2]
Wed, 21 Oct 2026 16:20:25 UTC (2,185 KB)
来源：arXiv cs.AI · arxiv.org