SENCA-st：通过跨注意力共享编码器整合空间转录组学与组织病理学以实现癌症病理学中的区域识别
SENCA-st: Integrating Spatial Transcriptomics and Histopathology with Cross Attention Shared Encoder for Region Identification in Cancer Pathology
AI总结：
针对现有方法在整合空间转录组学与组织病理学数据时存在的模态失衡问题，提出SENCA-st架构，通过共享编码器和交叉注意力保留双模态特征，在肿瘤异质性和微环境区域检测中超越现有方法。
摘要：
空间转录组学是一个新兴领域，可基于基因表达的空间分布识别功能区域。将转录组数据中的这种功能信息与组织病理学图像的结构数据整合是一个活跃的研究方向，应用于识别与癌症耐药性相关的肿瘤亚结构。当前的组织病理学-空间转录组区域分割方法存在缺陷：要么通过仅将组织病理学特征用于辅助处理空间转录组数据而突出空间转录组学，要么使用普通对比学习因仅促进共同特征而丢失功能信息，使组织病理学图像占主导。我们提出SENCA-st（带交叉注意力的共享编码器），保留两种模态特征，通过交叉注意力强调组织病理学结构相似但空间转录组功能不同的区域，在检测肿瘤异质性和肿瘤微环境区域方面超越最先进方法，这是临床关键方面。
英文摘要：
Spatial transcriptomics is an emerging field that enables the identification of functional regions based on the spatial distribution of gene expression. Integrating this functional information present in transcriptomic data with structural data from histopathology images is an active research area with applications in identifying tumor substructures associated with cancer drug resistance. Current histopathology-spatial-transcriptomic region segmentation methods suffer due to either making spatial transcriptomics prominent by using histopathology features just to assist processing spatial transcriptomics data or using vanilla contrastive learning that make histopathology images prominent due to only promoting common features losing functional information. In both extremes, the model gets either lost in the noise of spatial transcriptomics or overly smoothed, losing essential information. Thus, we propose our novel architecture SENCA-st (Shared Encoder with Neighborhood Cross Attention) that preserves the features of both modalities. More importantly, it emphasizes regions that are structurally similar in histopathology but functionally different on spatial transcriptomics using cross-attention. We demonstrate the superior performance of our model that surpasses state-of-the-art methods in detecting tumor heterogeneity and tumor micro-environment regions, a clinically crucial aspect.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
