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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationThird International Conference on Computer Vision and Pattern Analysis, ICCPA 2023
EditorsLinlin Shen, Guoqiang Zhong
PublisherSPIE
ISBN (Electronic)9781510667563
DOIs
StatePublished - 2023
Event3rd International Conference on Computer Vision and Pattern Analysis, ICCPA 2023 - Hangzhou, China
Duration: 7 Apr 20239 Apr 2023

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume12754
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference3rd International Conference on Computer Vision and Pattern Analysis, ICCPA 2023
Country/TerritoryChina
CityHangzhou
Period7/04/239/04/23

Keywords

  • Domain adaptation
  • autism spectrum disorder
  • autoencoder
  • low-rank representation
  • multi-site data

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