HFFST: A Hierarchical Feature Fusion Algorithm for Spatial Gene Expression Prediction Using Histopathology Images

The emergence of spatial transcriptomics has greatly advanced our understanding of disease mechanisms, identified novel therapeutic targets, and contributed to progress in personalized medicine. However, its high costs and technical complexity limit its widespread application. A promising alternative is to predict spatial gene expression from H&E-stained pathology images, yet existing methods have not fully exploited the hierarchical information from pathological images. We propose HFFST, a hierarchical feature fusion algorithm for predicting spatial gene expression from H&E-stained pathology images. Leveraging multi-level feature extraction and fusion from whole-slide images, our method employs a coarse-to-fine regression framework to predict spatial transcriptomic profiles. To validate the algorithm's performance, we conducted cross-validation on five public datasets and performed external validation using high-resolution Visium data from 10X Genomics. HFFST has shown promise in predicting spatial gene expression and identifying spatial regions, demonstrating certain advantages over state-of-the-art methods.