Self-supervised ensembled learning for autism spectrum classification / Manu GAUR in Research in Autism Spectrum Disorders, 107 (September 2023)  

Public ISBD [article]  inResearch in Autism Spectrum Disorders > 107 (September 2023) . - p.102223 Titre : Self-supervised ensembled learning for autism spectrum classification Type de document : Texte imprimé et/ou numérique Auteurs : Manu GAUR, Auteur ; Kunal CHATURVEDI, Auteur ; Dinesh Kumar VISHWAKARMA, Auteur ; Savitha RAMASAMY, Auteur ; Mukesh PRASAD, Auteur Article en page(s) : p.102223 Langues : Anglais (eng) Mots-clés : Autism spectrum disorder  Self-supervised learning  Pre-training  Classification  Ensembled learning Index. décimale : PER Périodiques Résumé : Purpose Deep learning has made remarkable progress in classifying autism spectrum disorder (ASD) using neuroimaging data. However, the current methods rely mainly on supervised learning, which requires a large amount of manually labeled data, making it an expensive and difficult task to scale. Methods To overcome this limitation, we propose a novel ensemble-based framework that learns a transferable and generalizable visual representation from different self-supervised features for the downstream task of ASD classification. This framework dynamically learns a superior representation by aggregating complementary information in the frequency domain from independent self-supervised features with limited data. Additionally, to address the information loss caused by the dimensionality reduction of 3D fMRI data, we propose a thresholding algorithm to optimally extract the most discriminant features from 2D rs-fMRI data. Results Experimental results demonstrate that the proposed method outperforms previous state-of-the-art methods by 19.69% on the ABIDE-1 dataset with a 10-fold cross-validation accuracy of 94.51%. Conclusion The proposed method learns a transferrable and generalizable ensembled representation by leveraging complementary information encoded in different self-supervised representations for ASD classification. En ligne : https://doi.org/10.1016/j.rasd.2023.102223 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=512 [article] Self-supervised ensembled learning for autism spectrum classification [Texte imprimé et/ou numérique] / Manu GAUR, Auteur ; Kunal CHATURVEDI, Auteur ; Dinesh Kumar VISHWAKARMA, Auteur ; Savitha RAMASAMY, Auteur ; Mukesh PRASAD, Auteur . - p.102223. Langues : Anglais (eng) in Research in Autism Spectrum Disorders > 107 (September 2023) . - p.102223 Mots-clés : Autism spectrum disorder  Self-supervised learning  Pre-training  Classification  Ensembled learning Index. décimale : PER Périodiques Résumé : Purpose Deep learning has made remarkable progress in classifying autism spectrum disorder (ASD) using neuroimaging data. However, the current methods rely mainly on supervised learning, which requires a large amount of manually labeled data, making it an expensive and difficult task to scale. Methods To overcome this limitation, we propose a novel ensemble-based framework that learns a transferable and generalizable visual representation from different self-supervised features for the downstream task of ASD classification. This framework dynamically learns a superior representation by aggregating complementary information in the frequency domain from independent self-supervised features with limited data. Additionally, to address the information loss caused by the dimensionality reduction of 3D fMRI data, we propose a thresholding algorithm to optimally extract the most discriminant features from 2D rs-fMRI data. Results Experimental results demonstrate that the proposed method outperforms previous state-of-the-art methods by 19.69% on the ABIDE-1 dataset with a 10-fold cross-validation accuracy of 94.51%. Conclusion The proposed method learns a transferrable and generalizable ensembled representation by leveraging complementary information encoded in different self-supervised representations for ASD classification. En ligne : https://doi.org/10.1016/j.rasd.2023.102223 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=512

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