HECLIP: Histology-Enhanced Contrastive Learning for Imputation of Transcriptomics Profiles
Journal:
arXiv
Published Date:
Jan 24, 2025
Abstract
Histopathology, particularly hematoxylin and eosin (H\&E) staining, plays a
critical role in diagnosing and characterizing pathological conditions by
highlighting tissue morphology. However, H\&E-stained images inherently lack
molecular information, requiring costly and resource-intensive methods like
spatial transcriptomics to map gene expression with spatial resolution. To
address these challenges, we introduce HECLIP (Histology-Enhanced Contrastive
Learning for Imputation of Profiles), an innovative deep learning framework
that bridges the gap between histological imaging and molecular profiling.
HECLIP is specifically designed to infer gene expression profiles directly from
H\&E-stained images, eliminating the need for expensive spatial transcriptomics
assays. HECLIP leverages an advanced image-centric contrastive loss function to
optimize image representation learning, ensuring that critical morphological
patterns in histology images are effectively captured and translated into
accurate gene expression profiles. This design enhances the predictive power of
the image modality while minimizing reliance on gene expression data. Through
extensive benchmarking on publicly available datasets, HECLIP demonstrates
superior performance compared to existing approaches, delivering robust and
biologically meaningful predictions. Detailed ablation studies further
underscore its effectiveness in extracting molecular insights from histology
images. Additionally, HECLIP's scalable and cost-efficient approach positions
it as a transformative tool for both research and clinical applications,
driving advancements in precision medicine. The source code for HECLIP is
openly available at https://github.com/QSong-github/HECLIP.
