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

doi:10.1117/12.2684421

1 August 2023 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

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Proceedings Volume 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023); 127543T (2023) https://doi.org/10.1117/12.2684421
Event: 2023 3rd International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 2023, Hangzhou, China

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.

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Lei Yu, Li Wang, Ming Cheng, Minhao Xue, and Lei Wang "Combining autoencoder and category-based low-rank domain adaptation method for multi-site ASD identification", Proc. SPIE 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 127543T (1 August 2023); https://doi.org/10.1117/12.2684421

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KEYWORDS

Matrices

Education and training

Feature extraction

Neurological disorders

Ablation

Data modeling

Data processing

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