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Communication dans un congrès

Learning Clusters in Autism Spectrum Disorder: Image-Based Clustering of Eye-Tracking Scanpaths with Deep Autoencode

Abstract : Autism spectrum disorder (ASD) is a lifelong condition characterized by social and communication impairments. This study attempts to apply unsupervised Machine Learning to discover clusters in ASD. The key idea is to learn clusters based on the visual representation of eye-tracking scanpaths. The clustering model was trained using compressed representations learned by a deep autoencoder. Our experimental results demonstrate a promising tendency of clustering structure. Further, the clusters are explored to provide interesting insights into the characteristics of the gaze behavior involved in autism.
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Communication dans un congrès
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https://hal-u-picardie.archives-ouvertes.fr/hal-03601455
Contributeur : Louise Dessaivre Connectez-vous pour contacter le contributeur
Soumis le : mardi 8 mars 2022 - 11:40:32
Dernière modification le : mercredi 9 mars 2022 - 03:25:41

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  • HAL Id : hal-03601455, version 1

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Mahmoud Elbattah, Romuald Carette, Gilles Dequen, Jean-Luc Guerin, Federica Cilia. Learning Clusters in Autism Spectrum Disorder: Image-Based Clustering of Eye-Tracking Scanpaths with Deep Autoencode. 2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2019, Berlin, Germany. pp.1417-1420. ⟨hal-03601455⟩

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