GASP: Gene-Aware Spatial Predictor for Histology-based Transcriptomics

Spatial transcriptomics (ST) provides unprecedented insights into tissue architecture but remains constrained by high costs and complex protocols. Predicting ST directly from ubiquitous Hematoxylin and Eosin (H&E) stained images offers a scalable alternative. However, existing computational methods predominantly adopt a ”vision-centric” paradigm, projecting passively extracted visual features into the gene space via a shared decoder. This approach overlooks the inherent heterogeneity of genes, failing to capture their unique morphological dependencies. In this paper, we propose the Gene-Aware Spatial Predictor (GASP), a novel framework that introduces a paradigm shift towards “gene-centric” modeling. GASP employs learnable Gene Queries that actively probe a multi-scale visual context pool—comprising local, neighborhood, and global features—via a Vision-Gene Cross-Attention mechanism. This allows each gene to adaptively retrieve its preferred morphological cues. Furthermore, a Gene-Specific Residual Decoding strategy is introduced to balance global expression trends with gene-specific variations. Extensive experiments on benchmark datasets demonstrate that GASP outperforms state-of-the-art methods, offering superior predictive accuracy for spatial gene expression modeling.