[1]CAI Hongshun,ZHANG Qiongmin,LONG Ying.Multiscale domain adaptation network for the auxiliary diagnosis of Alzheimer’s disease[J].CAAI Transactions on Intelligent Systems,2023,18(5):1090-1098.[doi:10.11992/tis.202205050]
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Multiscale domain adaptation network for the auxiliary diagnosis of Alzheimer’s disease

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 5 Page number: 1090-1098 Column: 学术论文—机器学习 Public date: 2023-09-05
Title:
Multiscale domain adaptation network for the auxiliary diagnosis of Alzheimer’s disease
Author(s):
CAI Hongshun;  ZHANG Qiongmin;  LONG Ying
College Of Computer Science And Engineering, Chongqing University of Technology, Chongqing 400054, China
Keywords:
Alzheimer’s disease; magnetic resonance imaging; domain shift; multiscale information; domain adaptation; joint training; adversarial learning; distance metrics
CLC:
TP391
DOI:
10.11992/tis.202205050
Abstract:
The paper proposes a multiscale domain adaptation network for the auxiliary diagnosis of Alzheimer’s disease. The network is designed to address the domain shift problem that traditional supervised learning ignores due to individual differences and different sites in magnetic resonance imaging (MRI) data. The network uses the Atrous Spatial Pyramid Pooling module in the three-dimensional convolutional neural network for feature extraction and fusion at multiple scales. Attention consistency loss is added to preserve the semantic information of interdomain transfer. Two domain discriminators and feature extractors are jointly trained in adversarial learning to achieve feature alignment of the source domain and target domain. Weight difference loss is added to prevent the domain discriminator from overfitting. A distance metric method based on the Maximum Density Divergence is introduced in the adversarial training to enhance the feature alignment of the two domain data. Experimental results reveal that this method exhibits superior recognition accuracy and robustness of MRI data facing the domain shift.
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