ISG:我能看到你的基因表达

Yan Yang, Liyuan Pan, Liu Liu, Eric A. Stone
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引用次数: 0

摘要

本文旨在从组织学切片图像中准确预测基因表达。这样的幻灯片图像具有较大的分辨率和稀疏分布的纹理。这些都阻碍了从幻灯片图像中提取和解释不同基因类型预测的区别特征。现有的基因表达方法主要是利用通用分量来过滤无纹理的区域,提取特征,跨区域进行特征的均匀聚合。然而,它们忽略了不同图像区域之间的间隙和相互作用,因此在基因表达任务中处于劣势。为此,我们提出了ISG框架,该框架通过三个新的模块来利用纹理丰富区域的判别特征之间的相互作用:1)香农选择模块,基于香农信息内容和所罗门诺夫理论,过滤掉无纹理的图像区域;2)特征提取网络,用于提取具有表现力的低维特征表示,以实现高分辨率图像之间的高效区域交互;(3)双注意网络关注具有期望基因表达特征的区域,并将它们聚集在一起进行预测任务。在标准基准数据集上进行的大量实验表明,所提出的ISG框架明显优于最先进的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ISG: I can See Your Gene Expression
This paper aims to predict gene expression from a histology slide image precisely. Such a slide image has a large resolution and sparsely distributed textures. These obstruct extracting and interpreting discriminative features from the slide image for diverse gene types prediction. Existing gene expression methods mainly use general components to filter textureless regions, extract features, and aggregate features uniformly across regions. However, they ignore gaps and interactions between different image regions and are therefore inferior in the gene expression task. Instead, we present ISG framework that harnesses interactions among discriminative features from texture-abundant regions by three new modules: 1) a Shannon Selection module, based on the Shannon information content and Solomonoff's theory, to filter out textureless image regions; 2) a Feature Extraction network to extract expressive low-dimensional feature representations for efficient region interactions among a high-resolution image; 3) a Dual Attention network attends to regions with desired gene expression features and aggregates them for the prediction task. Extensive experiments on standard benchmark datasets show that the proposed ISG framework outperforms state-of-the-art methods significantly.
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