Combining Autoencoder and Category-Based Low-Rank Domain Adaptation Method for Multi-Site ASD Identification

Lei Yu, Li Wang, Ming Cheng, Minhao Xue, Lei Wang

科研成果: 书/报告/会议事项章节会议稿件同行评审

1 引用 (Scopus)

摘要

With the development of deep learning in diagnosing autism spectrum disorder (ASD), multi-site resting-state functional magnetic resonance imaging (rs-fMRI) images have made excellent progress. However, there is a heterogeneous problem between the multi-site data caused by inconsistent data distribution. To address this problem, we propose a combining autoencoder and category-based low-rank domain adaptation (AECLR) method for multi-site ASD identification. The main idea is to extract non-linear features and alignment the distribution of these features. In the first stage, the unsupervised autoencoder is used to obtain the non-linear representation. In the second stage, the common structure between all domains was mined by the category-based low-rank constraints, which transform all source domain data and the target domain data into the common latent space and then the source domain data could be linearly represented by the target domain data. As a result, the ablation experiments of the AECLR method achieve independent performance and the AECLR method also obtain a satisfactory classification when compared with the state-of-the-art method.

源语言英语
主期刊名Third International Conference on Computer Vision and Pattern Analysis, ICCPA 2023
编辑Linlin Shen, Guoqiang Zhong
出版商SPIE
ISBN(电子版)9781510667563
DOI
出版状态已出版 - 2023
活动3rd International Conference on Computer Vision and Pattern Analysis, ICCPA 2023 - Hangzhou, 中国
期限: 7 4月 20239 4月 2023

出版系列

姓名Proceedings of SPIE - The International Society for Optical Engineering
12754
ISSN(印刷版)0277-786X
ISSN(电子版)1996-756X

会议

会议3rd International Conference on Computer Vision and Pattern Analysis, ICCPA 2023
国家/地区中国
Hangzhou
时期7/04/239/04/23

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