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Communication dans un congrès

Learning to Predict Autism Spectrum Disorder based on the Visual Patterns of Eye-tracking Scanpaths

Abstract : Autism spectrum disorder (ASD) is a lifelong condition generally characterized by social and communication impairments. The early diagnosis of ASD is highly desirable, and there is a need for developing assistive tools to support the diagnosis process in this regard. This paper presents an approach to help with the ASD diagnosis with a particular focus on children at early stages of development. Using Machine Learning, our approach aims to learn the eye-tracking patterns of ASD. The key idea is to transform eye-tracking scanpaths into a visual representation, and hence the diagnosis can be approached as an image classification task. Our experimental results evidently demonstrated that such visual representations could simplify the prediction problem, and attained a high accuracy as well. With simple neural network models and a relatively limited dataset, our approach could realize a quite promising accuracy of classification (AUC > 0.9).
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Communication dans un congrès
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Contributeur : Louise Dessaivre Connectez-vous pour contacter le contributeur
Soumis le : mardi 8 mars 2022 - 11:40:31
Dernière modification le : samedi 23 avril 2022 - 15:51:42

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Romuald Carette, Mahmoud Elbattah, Federica Cilia, Gilles Dequen, Jean-Luc Guerin, et al.. Learning to Predict Autism Spectrum Disorder based on the Visual Patterns of Eye-tracking Scanpaths. HEALTHINF: PROCEEDINGS OF THE 12TH INTERNATIONAL JOINT CONFERENCE ON BIOMEDICAL ENGINEERING SYSTEMS AND TECHNOLOGIES - VOL 5: HEALTHINF, 2019, Prague, Slovakia. pp.103-112, ⟨10.5220/0007402601030112⟩. ⟨hal-03601454⟩



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