PaSTel: Anchoring Histology in Spatial Transcriptomics via Multi-Scale Hierarchical Bio-Prior Contrastive Pretraining

[2608.14924] PaSTel: Anchoring Histology in Spatial Transcriptomics via Multi-Scale Hierarchical Bio-Prior Contrastive Pretraining Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2608.14924 (cs) [Submitted on 14 Aug 2026] Title: PaSTel: Anchoring Histology in Spatial Transcriptomics via Multi-Scale Hierarchical Bio-Prior Contrastive Pretraining Authors: Azim Dehghani Amirabad , Junchao Zhu , Pushpak Pati , Walid Abdelmoula , Tommaso Mansi , Rui Liao View a PDF of the paper titled PaSTel: Anchoring Histology in Spatial Transcriptomics via Multi-Scale Hierarchical Bio-Prior Contrastive Pretraining, by Azim Dehghani Amirabad and 5 other authors View PDF HTML (experimental) Abstract: Spatial transcriptomics (ST) links tissue morphology with molecular programs, motivating multimodal pretraining methods that align histology images with gene expression. However, existing approaches suffer from two key limitations: spatially informative gene selection is often dominated by ubiquitous housekeeping genes, leading to weakly discriminative representations, and independent spot-patch alignment fails to capture spatial dependencies that are critical for tissue organization. To address these challenges, we introduce PaSTel, a hierarchical multimodal pretraining framework that integrates biological priors at three levels. At the spot level, TF-IDF reweighting is used to identify spatially informative genes; at the functional level, curated KEGG pathways serve as anchors for encoding global biological semantics; and at the regional level, spatial clustering aggregates neighboring spots to model meso-scale tissue structure. Across multiple downstream tasks, PaSTel consistently outperforms existing vision and vision-omics encoders, demonstrating that incorporating multiscale biological priors yields more informative and transferable representations for spatial transcriptomics. Comments: This paper was accepted to the 3rd ICML 2026 Workshop on Multi-modal Foundation Models and Large Language Models for Life Sciences Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI) MSC classes: 68T07, cs.LG, q-bio.GN Cite as: arXiv:2608.14924 [cs.CV] (or arXiv:2608.14924v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.14924 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Azim Dehghani Amirabad [ view email ] [v1] Fri, 14 Aug 2026 22:26:17 UTC (5,091 KB) Full-text links: Access Paper: View a PDF of the paper titled PaSTel: Anchoring Histology in Spatial Transcriptomics via Multi-Scale Hierarchical Bio-Prior Contrastive Pretraining, by Azim Dehghani Amirabad and 5 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2026-08 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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