Spatially Gene Expression Prediction using Dual-Scale Contrastive Learning
Journal:
arXiv
Published Date:
Jun 30, 2025
Abstract
Spatial transcriptomics (ST) provides crucial insights into tissue
micro-environments, but is limited to its high cost and complexity. As an
alternative, predicting gene expression from pathology whole slide images (WSI)
is gaining increasing attention. However, existing methods typically rely on
single patches or a single pathology modality, neglecting the complex spatial
and molecular interactions between target and neighboring information (e.g.,
gene co-expression). This leads to a failure in establishing connections among
adjacent regions and capturing intricate cross-modal relationships. To address
these issues, we propose NH2ST, a framework that integrates spatial context and
both pathology and gene modalities for gene expression prediction. Our model
comprises a query branch and a neighbor branch to process paired target patch
and gene data and their neighboring regions, where cross-attention and
contrastive learning are employed to capture intrinsic associations and ensure
alignments between pathology and gene expression. Extensive experiments on six
datasets demonstrate that our model consistently outperforms existing methods,
achieving over 20% in PCC metrics. Codes are available at
https://github.com/MCPathology/NH2ST
