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English(EN) HiST: A Hierarchical Sparse Transformer for Cross-Modal Spatial Transcriptomics Modeling

HiST Transformer 增强空间转录组学建模

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了HiST，一种新颖的分层稀疏Transformer，用于跨模态空间转录组学建模。该模型通过有效处理稀疏、不规则分布的基因测量数据，解决了从组织学图像推断基因表达的挑战。HiST利用稀疏窗口注意力和分辨率变化算子来整合多尺度上下文，其运行时和内存的扩展基于观察到的位置而非整个切片区域。它还包含一个切片校准令牌来处理切片采集中的变化，从而提高了预测性能并减少了计算资源。 AI

影响 HiST提供了一种更有效的方法来整合基因表达数据与组织形态学，有望加速空间转录组学研究。

排序理由 该集群包含一篇详细介绍特定科学领域新模型架构的研究论文。

在 arXiv cs.CV 阅读 →

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HiST Transformer 增强空间转录组学建模

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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Weiyi Wu, Xinwen Xu, Xingjian Diao, Siting Li, Zhi Wei, Alma Andersson, Jiang Gui ·

    HiST：一种用于跨模态空间转录组建模的分层稀疏Transformer

    arXiv:2606.14251v1 Announce Type: new Abstract: Spatial transcriptomics (ST) links gene expression with tissue morphology but remains expensive and low-throughput, motivating surrogates that infer expression from routine histology. Whole-slide H&amp;E-to-ST inference pairs a giga…

  2. arXiv cs.CV TIER_1 English(EN) · Jiang Gui ·

    HiST：一种用于跨模态空间转录组建模的分层稀疏Transformer

    Spatial transcriptomics (ST) links gene expression with tissue morphology but remains expensive and low-throughput, motivating surrogates that infer expression from routine histology. Whole-slide H&E-to-ST inference pairs a gigapixel image with gene measurements at a sparse, irre…

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