生物医学工程研究2025，Vol.44Issue(2)：75-82,8.DOI:10.19529/j.cnki.1672-6278.2025.02.02
基于无监督跨域自适应的抑郁症识别算法研究
Research on unsupervised cross-domain adaptive based depression recognition algorithm
摘要
Abstract
In order to solve the inconsistent feature distribution between domains of multi-site resting-state functional magnetic resonance imaging(rs-fMRI)data,we proposed an unsupervised cross-domain adaptive depression recognition algorithm to make full use of multi-scale timing information,balance the feature distribution difference between the source domain and the target domain of multi-site rs-fMRI data.First,the inter-domain distribution difference was reduced through generative adversarial network,The do-main alignment mechanism with statistical feature matched with adversarial learning was employed to achieve unsupervised cross-do-main feature alignment between two domains.Then,an attention-guided multi-scale spatiotemporal graph convolution module was de-signed to extract and fuse multi-scale feature information.Based on the analysis of 681 subjects in the REST-meta-MDD dataset,the accuracy,area under the receiver operating characteristic curve,sensitivity,specificity and precision of this algorithm reached 67.57％,65.16％,89.19％,66.22％and 65.00％,respectively.The experimental results show that the algorithm has excellent recog-nition performance and can provide certain technical support for the clinical auxiliary diagnosis of major depressive disorder.关键词
抑郁症/静息态功能磁共振成像/域自适应/对抗学习/多尺度信息Key words
Major depressive disorder/Resting-state functional magnetic resonance imaging/Domain adaptation/Adversarial learning/Multi-scale information分类
基础医学引用本文复制引用
王辰雨,李翔,魏本征..基于无监督跨域自适应的抑郁症识别算法研究[J].生物医学工程研究,2025,44(2):75-82,8.基金项目
国家自然科学基金项目(62372280) （62372280）
山东省自然科学基金项目(20230F094,2024MF139) （20230F094,2024MF139）
青岛市科技惠民示范专项(23-2-8-smik-2-nsh) （23-2-8-smik-2-nsh）
齐鲁健康与卫生领军人才计划项目. （）
