H&Enium, Applying Foundation Models to Computational Pathology and Spatial Transcriptomics to Learn an Aligned Latent Space

Abstract

Bridging the gap from transcriptomic to imaging data at single-cell resolution is essential for understanding tumor biology and improving cancer diagnostics. Spatial transcriptomics enables mapping gene expression onto H&E images of segmented single cells, but remains limited by cost and throughput. We introduce H&Enium, a contrastive alignment framework that projects image and gene expression embeddings from foundation models into an aligned latent space using projection heads and a novel soft alignment target. This alignment enriches image-derived embeddings with transcriptomic context improving downstream tasks such as cell type classification and gene expression prediction. Additional evaluations on independent pathology datasets demonstrate superior generalization of our aligned representations over unaligned baselines. Our method offers a scalable path to enhance the utility of standard H&E imaging in both research and clinical settings.