This model infers spatial gene expression accurately in histological images, highlighting its robustness and versatility.
Spatial transcriptomics has revolutionized biological research but is limited by trade-offs between spatial resolution and gene coverage. Here we present SpaFoundation, a versatile visual foundational model with 80 million trainable parameters, pre-trained on 1.84 million histological image patches to learn general-purpose imaging representations. Using histology images alone, SpaFoundation accurately infers spatial gene expression and demonstrates strong robustness and transferability across tasks such as super-resolution inference, tumor detection, and spatial clustering.
No takes yet. Share an insight, caveat, or question.
Zhang et al. (2025) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: