用双路径模型融合图像与单细胞数据，精准预测组织中基因表达位置。
DUET: Dual-Paradigm Adaptive Expert Triage with Single-cell Inductive Prior for Spatial Transcriptomics Prediction
- 双路径并行：回归预测+记忆检索，动态分配权重提升精度
- 引入大规模单细胞数据作为生物约束，避免视觉相似却分子不一致
- 轻量适配器按空间上下文自动调整路径偏好，适合多场景空间转录组研究
从组织病理图像推断空间基因表达，可低成本补充空间转录组（ST）数据。现有方法仅将形态学与基因表达简单映射，视觉相似未必对应分子一致。单细胞数据规模远超ST数据，但尚未充分用于视觉-组学建模。当前方法多采用单一架构，难以兼顾表达灵活性与生物学真实性。为此，我们提出DUET，一种基于细胞归纳先验的双范式自适应专家调度框架。该框架融合参数化预测与基于记忆的检索，在互补路径间自适应融合输出。为缓解视觉不确定性，引入大规模单细胞参考数据，以分子状态作为生物学约束实现可靠学习。通过结构优化设计轻量级适配器，动态分配不同空间上下文中的路径偏好。在三个公开数据集上对多种基因规模的实验表明，DUET达到当前最优性能，各组件贡献稳定。代码已开源。
原文摘要 · Abstract (English)
Inferring spatially resolved gene expression from histology images offers a cost-effective complement to spatial transcriptomics (ST). However, existing methods reduce this task to a simple morphology-to-expression mapping, where visual similarity does not guarantee molecular consistency. Meanwhile, single-cell data has amassed rich resources far surpassing the scale of ST data, yet it remains underexplored in vision-omics modeling. Furthermore, current approaches commit to a monolithic paradigm with bottlenecks, unable to balance expressive flexibility with biological fidelity. To bridge these gaps, we propose DUET, a novel dual-paradigm framework that synergizes parametric prediction and memory-based retrieval under cellular inductive priors. DUET implements a parallel regression-retrieval paradigm, adaptively reconciling the outputs of its complementary pathways. To mitigate aleatoric vision ambiguity, we incorporate large-scale single-cell references to impose molecular states as biological constraints for faithful learning. Building upon structural refinement, we further design a lightweight adapter to dynamically assign branch preference across spatial contexts to achieve optimal performance. Extensive experiments on three public datasets across varied gene scales demonstrate that DUET achieves SOTA performance, with consistent gains contributed by each proposed component. Code is available at https://github.com/Junchao-Zhu/DUET
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