计算机科学 ›› 2022, Vol. 49 ›› Issue (3): 129-133.doi: 10.11896/jsjkx.201100152

• 数据库&大数据&数据科学 • 上一篇    下一篇

面向多中心数据的超图卷积神经网络及应用

周海榆, 张道强   

  1. 1 南京航空航天大学计算机科学与技术学院 南京210016
    2 南京航空航天大学模式分析与机器智能工信部重点实验室 南京210016
  • 收稿日期:2020-11-23 修回日期:2021-12-08 出版日期:2022-03-15 发布日期:2022-03-15
  • 通讯作者: 张道强(dqzhang@nuaa.edu.cn)
  • 作者简介:(fishzzhou@163.com)
  • 基金资助:
    国家自然科学基金(61876082,61861130366,61732006)

Multi-site Hyper-graph Convolutional Neural Networks and Application

ZHOU Hai-yu, ZHANG Dao-qiang   

  1. 1 College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China
    2 MIIT Key Laboratory of Pattern Analysis and Machine Intelligence,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China
  • Received:2020-11-23 Revised:2021-12-08 Online:2022-03-15 Published:2022-03-15
  • About author:ZHOU Hai-yu,born in 1994,postgra-duate.His main research interests include computer vision and medical image analysis.
    ZHANG Dao-qiang,born in 1978,professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include machine lear-ning,pattern recognition,data mining and medical image analysis.
  • Supported by:
    National Natural Science Foundation of China(61876082,61861130366,61732006)

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。

关键词: 超图卷积网络, 多中心数据, 脑疾病诊断, 数据异质性, 图卷积网络

Abstract: 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 multi-site 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.

Key words: Brain diseases diagnosis, Data heterogeneity, Graph convolutional networks, Hyper-graph convolutional networks, Multi-site dataset

中图分类号: 

  • TP181
[1]SCARSELLI F,GORI M,TSOI A C,et al.The Graph NeuralNetwork Model[J].IEEE Transactions on Neural Networks,2009,20(1):61-80.
[2]BRUNA J,ZAREMBA W,SZLAM A.Yann LeCun:SpectralNetworks and Locally Connected Networks on Graphs[C]//International Conference on Learning Representations.2014.
[3]DEFFERRARD M,BRESSON X,VANDERGHEYNST P.Convolutional neural networks on graphs with fast localized spectral filtering [C]//Advances in Neural Information Processing Systems.2016:3844-3852.
[4]KIPF T N,WELLING M.Semi-supervised classification withgraph convolutional networks[C]//International Conference on Learning Representations.2017.
[5]ATWOOD J,TOWSLEY D.Diffusion convolutional neural networks[C]//Advances in Neural Information Processing Systems.2016:1993-2001.
[6]DUVENAUD D K,MACLAURIN D,IPARRAGUIRRE J,et al.Convolutional networks on graphs for learning molecular fingerprints [C]//Advances in Neural Information Processing Systems.2015:2224-2232.
[7]HAMILTON W,YING Z T,LESKOVEC J.Inductive representation learning on large graphs [C]//Advances in Neural Information Processing Systems.2017:1024-1034.
[8]KTENA S I,PARISOT S,FERRANTE E,et al.Distance metric learning using graph convolutional networks:Application to functional brain networks [C]//International Conference on Medical Image Computing and Computer-Assisted Intervention.Springer,2017:469-477.
[9]PARISOT S,KTENA S I,FERRANTE E,et al.Disease predic-tion using graph convolutional networks:application to autism spectrum disorder and Alzheimer’s disease[J].Medical Image Analysis,2018,48:117-130.
[10]HUANG Y C,LIU Q S,METAXAS D.Video object segmentation by hypergraph cut [C]//IEEE Conference on Computer Vision and Pattern Recognition.2009:1738-1745.
[11]ZHANG Z Z,LIN H J,GAO Y,et al.Dynamic HypergraphStructure Learning [C]//Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence.2018:3162-3169.
[12]FENG Y F,YOU H X,ZHANG Z Z,et al.Hypergraph neural networks [C]//Proceedings of the AAAI Conference on Artificial Intelligence.2019:3558-3565.
[13]BAI S,ZHANG F H,TORR P H S.Hypergraph convolutionand hypergraph attention[J].arXiv:1901.08150,2019.
[14]JIANG J W,WEI Y X,FENG Y F,et al.Dynamic hypergraph neural networks[C]//Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence.2019:2635-2641.
[15]MARTINO A D,YAN C G,LI Q,et al.The autism brain ima-ging data exchange:towards a large-scale evaluation of the intrinsic brain architecture in autism[J].Molecular Psychiatry,2014,19(6):659-667.
[16]MAO B C,HUANG J S,ZHANG D Q.Node based row filter convolutional neural network for brain network classification [C]//Pacific Rim International Conference on Artificial Intelligence.2018:1069-1080.
[17]KTENA S I,PARISOT S,FERRANTE E,et al.Metric learning with spectral graph convolutions on brain connectivity networks[J].NeuroImage,2018,169:431-442.
[1] 汪鸣, 彭舰, 黄飞虎.
基于多时间尺度时空图网络的交通流量预测模型
Multi-time Scale Spatial-Temporal Graph Neural Network for Traffic Flow Prediction
计算机科学, 2022, 49(8): 40-48. https://doi.org/10.11896/jsjkx.220100188
[2] 李健智, 王红玲, 王中卿.
基于图卷积网络的专利摘要自动生成研究
Automatic Generation of Patent Summarization Based on Graph Convolution Network
计算机科学, 2022, 49(6A): 172-177. https://doi.org/10.11896/jsjkx.210400117
[3] 赵小虎, 叶圣, 李晓.
多算法融合的骨骼重建信息动作分类方法
Multi-algorithm Fusion Behavior Classification Method for Body Bone Information Reconstruction
计算机科学, 2022, 49(6): 269-275. https://doi.org/10.11896/jsjkx.210500070
[4] 潘志豪, 曾碧, 廖文雄, 魏鹏飞, 文松.
基于交互注意力图卷积网络的方面情感分类
Interactive Attention Graph Convolutional Networks for Aspect-based Sentiment Classification
计算机科学, 2022, 49(3): 294-300. https://doi.org/10.11896/jsjkx.210100180
[5] 解宇, 杨瑞玲, 刘公绪, 李德玉, 王文剑.
基于动态拓扑图的人体骨架动作识别算法
Human Skeleton Action Recognition Algorithm Based on Dynamic Topological Graph
计算机科学, 2022, 49(2): 62-68. https://doi.org/10.11896/jsjkx.210900059
[6] 程思伟, 葛唯益, 王羽, 徐建.
BGCN:基于BERT和图卷积网络的触发词检测
BGCN:Trigger Detection Based on BERT and Graph Convolution Network
计算机科学, 2021, 48(7): 292-298. https://doi.org/10.11896/jsjkx.200500133
[7] 宋龙泽, 万怀宇, 郭晟楠, 林友芳.
面向出租车空载时间预测的多任务时空图卷积网络
Multi-task Spatial-Temporal Graph Convolutional Network for Taxi Idle Time Prediction
计算机科学, 2021, 48(7): 112-117. https://doi.org/10.11896/jsjkx.201000089
[8] 宋元隆, 吕光宏, 王桂芝, 贾吾财.
基于图卷积神经网络的SDN网络流量预测
SDN Traffic Prediction Based on Graph Convolutional Network
计算机科学, 2021, 48(6A): 392-397. https://doi.org/10.11896/jsjkx.200800090
[9] 吕明琪, 洪照雄, 陈铁明.
一种融合时空关联与社会事件的交通流预测方法
Traffic Flow Forecasting Method Combining Spatio-Temporal Correlations and Social Events
计算机科学, 2021, 48(2): 264-270. https://doi.org/10.11896/jsjkx.200300098
[10] 叶松涛, 周扬正, 范红杰, 陈正雷.
融合因果关系和时空图卷积网络的人体动作识别
Joint Learning of Causality and Spatio-Temporal Graph Convolutional Network for Skeleton- based Action Recognition
计算机科学, 2021, 48(11A): 130-135. https://doi.org/10.11896/jsjkx.201200205
[11] 蒋宗礼, 李苗苗, 张津丽.
基于融合元路径图卷积的异质网络表示学习
Graph Convolution of Fusion Meta-path Based Heterogeneous Network Representation Learning
计算机科学, 2020, 47(7): 231-235. https://doi.org/10.11896/jsjkx.190600085
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!