用于脑部疾病诊断的知识感知多站点自适应图转换器

Xuegang Song, Kaixiang Shu, Peng Yang, Cheng Zhao, Feng Zhou, Alejandro F Frangi, Xiaohua Xiao, Lei Dong, Tianfu Wang, Shuqiang Wang, Baiying Lei
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摘要

由于成像特征复杂、样本量大,通过静息态功能磁共振成像(rs-fMRI)诊断脑部疾病通常受到限制。在脑部疾病诊断方面,图卷积网络(GCN)通过捕捉个体与群体之间的相互作用取得了显著的成功。然而,它主要有三个局限性:1) 以往的 GCN 方法在构建边缘时考虑了非成像信息,但忽略了特征对非成像信息的敏感性差异。2) 以往的 GCN 方法只关注建立主体(即个体和群体)之间的相互作用,忽略了特征之间的本质关系。3) 多站点数据增加了样本量,有助于分类器的训练,但站点间的异质性在一定程度上限制了分类器的性能。本文提出了一种知识感知的多站点自适应图变换器来解决上述问题。首先,我们评估特征对每一条非图像信息的敏感度,然后构建特征敏感子图和特征不敏感子图。其次,在融合上述子图之后,我们集成了一个变换器模块来捕捉特征之间的内在关系。第三,我们设计了一个域自适应 GCN,使用多个损失函数项来缓解数据异质性,并生成最终分类结果。最后,我们在两项脑部疾病诊断任务中验证了所提出的框架。实验结果表明,所提出的框架可以达到最先进的性能。
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Knowledge-aware Multisite Adaptive Graph Transformer for Brain Disorder Diagnosis.

Brain disorder diagnosis via resting-state functional magnetic resonance imaging (rs-fMRI) is usually limited due to the complex imaging features and sample size. For brain disorder diagnosis, the graph convolutional network (GCN) has achieved remarkable success by capturing interactions between individuals and the population. However, there are mainly three limitations: 1) The previous GCN approaches consider the non-imaging information in edge construction but ignore the sensitivity differences of features to non-imaging information. 2) The previous GCN approaches solely focus on establishing interactions between subjects (i.e., individuals and the population), disregarding the essential relationship between features. 3) Multisite data increase the sample size to help classifier training, but the inter-site heterogeneity limits the performance to some extent. This paper proposes a knowledge-aware multisite adaptive graph Transformer to address the above problems. First, we evaluate the sensitivity of features to each piece of non-imaging information, and then construct feature-sensitive and feature-insensitive subgraphs. Second, after fusing the above subgraphs, we integrate a Transformer module to capture the intrinsic relationship between features. Third, we design a domain adaptive GCN using multiple loss function terms to relieve data heterogeneity and to produce the final classification results. Last, the proposed framework is validated on two brain disorder diagnostic tasks. Experimental results show that the proposed framework can achieve state-of-the-art performance.

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