FmH2ST: foundation model-based spatial transcriptomics generation from histological images.

ABSTRACT: Spatial transcriptomics (ST) reveals gene expression distributions within tissues. Yet, predicting spatial gene expression from histological images still faces the challenges of limited ST data that lack prior knowledge, and insufficient capturing of inter-slice heterogeneity and intra-slice complexity. To tackle these challenges, we introduce FmH2ST, a foundation model-based method for spatial gene expression prediction. Equipped with powerful foundation models pretrained on massive images, FmH2ST employs a dual-branch framework to integrate prior knowledge from foundation model and fine-grained details from spot images. The foundation model branch employs a multilevel feature extraction strategy to obtain enriched features with slice context for capturing inter-slice heterogeneity, and a

SUBMITTER: Wang Y 

PROVIDER: S-EPMC12418390 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature