[Submitted on 6 Jul 2026]
Title:Multi-Teacher Contrastive Distillation for Edge-Efficient Pathology Foundation Models
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Abstract:Computational pathology foundation models (PFMs) have advanced whole-slide image analysis. However, their size and inference cost hinder local deployment in pathology departments. We propose MuCoDi, a pretraining framework that distills frozen tile embeddings from multiple PFMs into compact edge-oriented encoders. Instead of regressing individual teacher features, MuCoDi trains lightweight MobileOne and RepViT students with a contrastive distillation objective adapted from MoCo v3, where cached Virchow2, UNI2, and H-Optimus-1 embeddings replace momentum-encoder keys. We pretrain students on 14.3M TCGA tiles from only 11.8K WSIs and evaluate frozen encoders on 23 clinically curated downstream classification tasks. RepViT-based MuCoEdge students retain near-teacher performance while reducing model size by orders of magnitude: MuCoEdge-R2.3 and MuCoEdge-R1.5 reach 71.0% external AUROC, within 0.8 percentage points of the best teacher (Virchow2, 71.8%), while MuCoEdge-R2.3 obtains the best external F1 and the second-best AUPRC (51.8% and 53.3%). MuCoEdge-R1.0 reaches 70.9% AUROC with only 6.4M parameters and 1.12 GFLOPs. On a Raspberry Pi 5, sub-million-parameter MobileOne students achieve up to 605-fold single-tile speedup over Virchow2 while retaining 66.5% to 66.9% external AUROC, demonstrating that PFM-quality pathology representations can be moved toward practical edge deployment. Code is available at this https URL.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.05533 [cs.CV]
(or arXiv:2607.05533v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.05533
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Tim Lenz [view email] [v1] Mon, 6 Jul 2026 18:14:37 UTC (914 KB)
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