MOSAIC：迈向计算病理学基础模型的通用潜在空间
MOSAIC: Towards a Universal Latent Space for Computational Pathology Foundation Models
AI 导读
MOSAIC 是一项系统性研究，分析了 12 个 patch 级和 6 个 slide 级病理学基础模型（PFM）潜在空间的相似性、对齐与互操作性。研究量化了不同模型表征的一致性，考察架构、预训练目标、网络深度和放大倍数对潜在几何的影响，并评估多种将独立学习表征映射到公共潜在空间的对齐策略，通过跨模型一致性、检索、几何保持和分子与形态预测的多实例学习任务进行评价。
正文 · 原文
Abstract:Pathology foundation models (PFMs) differ substantially in architecture, pretraining objective, training data and representation dimensionality, yet it remains unclear whether they learn fundamentally different feature spaces or alternative coordinate systems of a shared underlying representation. We present MOSAIC, a systematic study of latent-space similarity, alignment and interoperability across $12$ patch-level and $6$ slide-level PFMs. We first quantify representational agreement using complementary similarity measures and analyze how architecture, pretraining objective, network depth and magnification influence latent geometry. We then evaluate multiple alignment strategies for mapping independently learned representations into a common latent space. The resulting representations are evaluated through cross-model agreement, retrieval, geometry preservation, and downstream multiple-instance learning across molecular and morphological prediction tasks. MOSAIC provides a unified framework for studying whether heterogeneous PFMs can move beyond representational similarity toward a shared and functionally transferable pathology representation space. Our code, benchmarks and additional results are available at this https URL.
| Comments: | Accepted at WACV 2027 |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.33059 [cs.CV] |
| (or arXiv:2610.33059v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.33059 arXiv-issued DOI via DataCite |
Submission history
From: Dibaloke Chanda [view email]
[v1]
Mon, 19 Oct 2026 10:19:47 UTC (3,041 KB)
来源：arXiv cs.AI · arxiv.org