用双尺度对比学习，从病理切片预测基因表达
Spatially Gene Expression Prediction using Dual-Scale Contrastive Learning
- 设计双分支结构，融合目标区域与邻近区域的病理和基因信息
- 在6个数据集上PCC超过20%提升，显著优于现有方法
- 适合关注组织微环境与基因互作的研究者
空间转录组学（ST）为组织微环境研究提供了关键洞察，但受限于高昂成本与复杂性。作为替代方案，利用病理全切片图像（WSI）预测基因表达正日益受到关注。然而，现有方法多依赖单一图像块或单一病理模态，忽视了目标区域与邻近区域间的复杂空间与分子交互（如基因共表达），导致难以建立相邻区域间关联并捕捉跨模态复杂关系。为此，我们提出NH2ST框架，整合空间上下文及病理与基因双重模态进行基因表达预测。模型包含查询分支与邻域分支，分别处理目标图像块及其邻近区域的病理与基因数据，通过交叉注意力与对比学习捕捉内在关联并确保病理与基因表达对齐。在六个数据集上的大量实验表明，本模型持续优于现有方法，在PCC指标上提升超20%。代码已开源。
原文摘要 · Abstract (English)
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
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