Hyperbolic Contrastive Learning with Entailment for Spatial Transcriptomics
Spatial Transcriptomics (ST) has transformed biomedical research by enabling the spatial mapping of gene expression across tissue sections. However, high operational costs, specialized equipment requirements, and sensitivity to experimental noise limit the accessibility and scalability of ST. Recent computer vision approaches aim to overcome these limitations by predicting spatial gene expression directly from histopathology images. While effective, current approaches often suffer from gene expression over-smoothing and overly uniform predictions across tissue regions, suggesting that further progress depends on learning representations that reflect the hierarchical and asymmetric structu...
| Authors | Daniela Vega, Paula Cárdenas, Hannah Ceballos et al. |
|---|---|
| Primary category | cs.CV |
| Published | 14 September 2026 |
| Last updated at source | 14 September 2026 |
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