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

• URL: http://arxiv.org/abs/2608.14924v1
• Date: Fri, 14 Aug 2026 22:26:17 GMT
• Title: PaSTel: Anchoring Histology in Spatial Transcriptomics via Multi-Scale Hierarchical Bio-Prior Contrastive Pretraining
• Abstract summary: PaSTel is a hierarchical multimodal pretraining framework that integrates biological priors at three levels.<n>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.<n>At the regional level, spatial clustering aggregates neighboring spots to model meso-scale tissue structure.
• Score: 4.434048652551508
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• PathAR: Structure-First Autoregressive Synthesis of Multimodal Pathology Images [51.428093790826814]
  We propose pathology Autorgressive modeling (PathAR) to factorize structure and appearance for modality-label-conditioned pathology generation.<n>PathAR stabilizes morphology under heterogeneous modality-specific appearances and enables spatially aligned image-mask pair generation.
  arXiv  Detail & Related papers  (2026-06-01T01:43:48Z)
• Multi-View Synergistic Learning with Vision-Language Adaption for Low-Resource Biomedical Image Classification [59.24009931000134]
  Multi-View Synergistic Learning (MVSL) is a unified framework that addresses adaptation paradigms, representation granularity, and disease semantic relationships.<n>MVSL decouples the adaptation of visual and textual encoders to respect their distinct representational characteristics.<n>It further introduces multi-granularity contrastive learning to explicitly model both global image semantics and localized lesion-level evidence.<n>MVSL consistently outperforms state-of-the-art methods in few-shot and zero-shot classification settings.
  arXiv  Detail & Related papers  (2026-04-27T02:41:27Z)
• Learning to Synergize Semantic and Geometric Priors for Limited-Data Wheat Disease Segmentation [51.06355116973389]
  We propose a framework that treats wheat disease segmentation as a coupled task of disease-specific semantic perception and disease boundary localization.<n>Our core insight is that pretrained DINOv2 provides robust category-aware semantic priors to handle appearance shifts.<n>We show that SGPer consistently achieves state-of-the-art performance on wheat disease and organ segmentation benchmarks.
  arXiv  Detail & Related papers  (2026-04-07T04:19:39Z)
• Hierarchy-Guided Multimodal Representation Learning for Taxonomic Inference [17.708423679075075]
  We present two end-to-end variants for hierarchy-aware multimodal learning.<n>CLiBD-HiR introduces Hierarchical Information Regularization (HiR) to shape embedding geometry across taxonomic levels.<n>CLiBD-HiR-Fuse trains a lightweight fusion predictor that supports image-only, DNA-only, or joint inference.
  arXiv  Detail & Related papers  (2026-03-26T15:47:03Z)
• Fusing Pixels and Genes: Spatially-Aware Learning in Computational Pathology [46.83014413674925]
  STAMP is a spatial transcriptomics-augmented multimodal pathology representation learning framework.<n>Our study shows that self-supervised, gene-guided training provides a robust and task-agnostic signal for learning pathology image representations.<n>We validate STAMP across six datasets and four downstream tasks, where it consistently achieves strong performance.
  arXiv  Detail & Related papers  (2026-02-15T00:59:13Z)
• MultiST: A Cross-Attention-Based Multimodal Model for Spatial Transcriptomic [21.236918431473466]
  We propose MultiST, a unified framework that models spatial topology, gene expression, and tissue morphology through cross-attention-based fusion.<n>We evaluated the proposed method on 13 diverse ST datasets spanning two organs, including human brain cortex and breast cancer tissue.
  arXiv  Detail & Related papers  (2026-01-19T19:11:03Z)
• HiFusion: Hierarchical Intra-Spot Alignment and Regional Context Fusion for Spatial Gene Expression Prediction from Histopathology [7.982889842329205]
  HiFusion is a novel deep learning framework that integrates two complementary components.<n>We show that HiFusion achieves state-of-the-art performance across both 2D slide-wise cross-validation and more challenging 3D sample-specific scenarios.<n>These results underscore HiFusion's potential as a robust, accurate, and scalable solution for ST inference from routine histopathology.
  arXiv  Detail & Related papers  (2025-11-17T04:47:39Z)
• MIRROR: Multi-Modal Pathological Self-Supervised Representation Learning via Modality Alignment and Retention [57.044719143401664]
  Histopathology and transcriptomics are fundamental modalities in oncology, encapsulating the morphological and molecular aspects of the disease.<n>We present MIRROR, a novel multi-modal representation learning method designed to foster both modality alignment and retention.<n>Extensive evaluations on TCGA cohorts for cancer subtyping and survival analysis highlight MIRROR's superior performance.
  arXiv  Detail & Related papers  (2025-03-01T07:02:30Z)
• Multi-modal Spatial Clustering for Spatial Transcriptomics Utilizing High-resolution Histology Images [1.3124513975412255]
  spatial transcriptomics (ST) enables transcriptome-wide gene expression profiling while preserving spatial context. Current spatial clustering methods fail to fully integrate high-resolution histology image features with gene expression data. We propose a novel contrastive learning-based deep learning approach that integrates gene expression data with histology image features.
  arXiv  Detail & Related papers  (2024-10-31T00:32:24Z)
• Revisiting Adaptive Cellular Recognition Under Domain Shifts: A Contextual Correspondence View [49.03501451546763]
  We identify the importance of implicit correspondences across biological contexts for exploiting domain-invariant pathological composition. We propose self-adaptive dynamic distillation to secure instance-aware trade-offs across different model constituents.
  arXiv  Detail & Related papers  (2024-07-14T04:41:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.