Tianyu Liu

A masked-expression Transformer trained on 983 paired H&E and spatial transcriptomics slides predicts 38,984 genes from histology without fine-tuning, leading HEST-Bench and improving survival and mutation prediction.

spEMO integrates histopathology and spatial multi-omic data using foundation models, improving performance in spatial analysis and medical report generation.

PathOmCLIP infers spatial gene expression from histology images by combining pathology and single-cell RNA-seq foundation models, achieving better gene expression prediction than existing methods.