[Submitted on 19 Oct 2026]
Title:MOSAIC: Towards a Universal Latent Space for Computational Pathology Foundation Models
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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)
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