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作者:

Ji, Junzhong (Ji, Junzhong.) (学者:冀俊忠) | Zhang, Yaqin (Zhang, Yaqin.)

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摘要:

Recently, clinical phenotypic semantic information has begun to play an important role in some brain network classification methods based on deep learning. However, most current methods only consider the phenotypic semantic information of individual brain networks but ignore the potential phenotypic characteristics among group brain networks. To address this problem, we present a deep hashing mutual learning (DHML)-based brain network classification method. Specifically, we first design a separable CNN-based deep hashing learning to extract individual topological features of brain networks and map them into hash codes. Secondly, we construct a group brain network relationship graph based on the similarity of phenotypic semantic information, in which each node is a brain network, and the properties of the nodes are the individual features extracted in the previous step. Then, we adopt a GCN-based deep hashing learning to extract the group topological features of the brain network and map them to hash codes. Finally, the two deep hashing learning models perform mutual learning by measuring the distribution differences between the hash codes to achieve the interaction of individual and group features. The experimental results on the three commonly used brain atlases (AAL Atlas, Dosenbach160 Atlas, and CC200 Atlas) of the ABIDE I dataset show that our proposed DHML method achieves optimal classification performance compared with some state-of-the-art methods.

关键词:

Brain network classification deep mutual learning separable convolution deep hashing learning

作者机构:

  • [ 1 ] [Ji, Junzhong]Beijing Univ Technol, Beijing Artificial Intelligence Inst, Fac Informat Technol, Beijing Municipal Key Lab Multimedia & Intelligent, Beijing 100124, Peoples R China
  • [ 2 ] [Zhang, Yaqin]Beijing Univ Technol, Beijing Artificial Intelligence Inst, Fac Informat Technol, Beijing Municipal Key Lab Multimedia & Intelligent, Beijing 100124, Peoples R China

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来源 :

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS

ISSN: 2168-2194

年份: 2023

期: 9

卷: 27

页码: 4489-4499

7 . 7 0 0

JCR@2022

ESI学科: COMPUTER SCIENCE;

ESI高被引阀值:19

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