Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer's disease
Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer's disease
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DOI：
10.1016/j.media.2018.06.001
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发表时间：
2018-08-01
影响因子：
10.9
通讯作者：
Rueckert, Daniel
中科院分区：
文献类型：
--
作者：
Parisot, Sarah;Ktena, Sofia Ira;Rueckert, Daniel
图被广泛用作捕获表示为图中节点的各个元素之间的交互的自然框架。具体地说，在医学应用中，节点可以表示潜在大量人群(患者或健康对照)中的个人，并伴随着一组特征，而图形边以直观的方式合并对象之间的关联。这种表示允许在疾病分类任务中同时合并丰富的成像和非成像信息以及单个主题特征。在疾病预测的背景下，以前的基于图的监督或非监督学习方法只关注对象之间的成对相似性，而忽略了个体特征和特征，或者更确切地说，依赖于特定于对象的成像特征向量并且不能对它们之间的交互进行建模。在这篇文章中，我们提出了一个通用框架的全面评估，该框架利用成像和非成像信息，可用于大规模人群的脑分析。该框架利用了图卷积网络(GCNS)，将种群表示为稀疏图，其中节点与基于成像的特征向量相关联，而表型信息被整合为边权重。广泛的评估探索了这一框架的每个单独组成部分对疾病预测业绩的影响，并进一步将其与不同的基线进行了比较。该框架的性能在两个具有不同基础数据的大型数据集上进行了测试，分别用于预测自闭症谱系障碍和转换为阿尔茨海默病。我们的分析表明，我们的新框架在两个数据库上的分类结果都得到了改善，在ABST和ADNI上的分类准确率分别为70.4%和80.0%。(C)2018爱思唯尔B.V.保留所有权利。
Graphs are widely used as a natural framework that captures interactions between individual elements represented as nodes in a graph. In medical applications, specifically, nodes can represent individuals within a potentially large population (patients or healthy controls) accompanied by a set of features, while the graph edges incorporate associations between subjects in an intuitive manner. This representation allows to incorporate the wealth of imaging and non-imaging information as well as individual subject features simultaneously in disease classification tasks. Previous graph-based approaches for supervised or unsupervised learning in the context of disease prediction solely focus on pairwise similarities between subjects, disregarding individual characteristics and features, or rather rely on subject-specific imaging feature vectors and fail to model interactions between them. In this paper, we present a thorough evaluation of a generic framework that leverages both imaging and non-imaging information and can be used for brain analysis in large populations. This framework exploits Graph Convolutional Networks (GCNs) and involves representing populations as a sparse graph, where its nodes are associated with imaging-based feature vectors, while phenotypic information is integrated as edge weights. The extensive evaluation explores the effect of each individual component of this framework on disease prediction performance and further compares it to different baselines. The framework performance is tested on two large datasets with diverse underlying data, ABIDE and ADNI, for the prediction of Autism Spectrum Disorder and conversion to Alzheimer's disease, respectively. Our analysis shows that our novel framework can improve over state-of-the-art results on both databases, with 70.4% classification accuracy for ABIDE and 80.0% for ADNI. (C) 2018 Elsevier B.V. All rights reserved.