DFCE-KanT: predicting spatial gene expression from histology images via contrastive learning
Abstract Motivation Spatial transcriptomics (ST) can quantitatively characterize the spatial molecular profile in tissues and yet is costly to be applied at a large scale. One viable, a low-cost alternative would be to directly predict spatial gene expression from routine hematoxylin eosin (H&E) stained WSIs. Yet, the...