摘要
近年来,图神经网络在神经性脑疾病诊断中的应用引起了广泛关注。然而,现有研究中使用的图通常只是基于简单的点对点连接,无法反映3个或更多受试者之间的复杂关联,尤其是在多中心数据集中,即由不同医疗机构所使用的不同采集设备和不同受试人群而集成的具有异质性的数据集。为解决医疗影像数据中存在的多中心异质性问题,提出了一种多中心超图数据结构来描述多中心数据之间的关系。这种超图由两种不同的超边构成,一种是描述单个中心内部关系的中心内超边,另一种是描述不同中心之间关系的跨中心超边。另外,还提出了一种超图卷积神经网络来学习节点的特征表示,这种超图卷积由两部分构成,第一部分是超图节点卷积,第二部分是超边卷积。在两个多中心数据集上的实验结果证明了所提方法的有效性。
Recently,the exploitation of graph neural networks for neurological brain disorder diagnosis has attracted much attention.However,the graphs used in the existing studies are usually based on the pairwise connections of different nodes,and thus cannot reflect the complex correlation of three or more subjects,especially in the multi-site dataset,i.e.,the dataset collected from different medical institutions with the problem of data heterogeneity resulted from various scanning parameters or subject population.To address this issue,a multi-site hypergraph data structure is proposed to describe the relationship between multisite data.This hypergraph consists of two types hyper-edge,one is intra-site hyper-edge that describes the relationship within the site,and the other is inter-site hyper-edge that describes relationship between different sites.Also,a hypergraph convolutional network is proposed to learn the feature representation of each node.The hypergraph convolution consists of two parts:the first part is the hypergraph node convolution,the second part is the super edge convolution.Experimental results on two multi-site datasets can also validate the effectiveness of the proposed method.
作者
周海榆
张道强
ZHOU Hai-yu;ZHANG Dao-qiang(College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China;MIIT Key Laboratory of Pattern Analysis and Machine Intelligence,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China)
出处
《计算机科学》
CSCD
北大核心
2022年第3期129-133,共5页
Computer Science
基金
国家自然科学基金(61876082,61861130366,61732006)。
关键词
多中心数据
数据异质性
脑疾病诊断
图卷积网络
超图卷积网络
Multi-site dataset
Data heterogeneity
Brain diseases diagnosis
Graph convolutional networks
Hyper-graph convolutional networks
