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Siamese Verification Framework for Autism Identification During Infancy Using Cortical Path Signature Features

Zhang, X; Ding, X; Wu, Z; Xia, J; Ni, H; Xu, X; Liao, L; ... Li, G; + view all (2020) Siamese Verification Framework for Autism Identification During Infancy Using Cortical Path Signature Features. In: Proceedings of the 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). (pp. pp. 395-398). IEEE: Iowa City, IA, USA. Green open access

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Abstract

Autism spectrum disorder (ASD) is a complex neurodevelopmental disability, which is lack of biologic diagnostic markers. Therefore, exploring the ASD Identification directly from brain imaging data has been an important topic. In this work, we propose the Siamese verification model to identify ASD using 6 and 12 months cortical features. Rather than directly classifying a testing subject is ASD or not, we determine whether it has the same or different label with the reference subject who has been successfully diagnosed. Then, based on the comparison to all the reference subjects, we can predict the label of the testing subject. The advantage of modeling the classification problem as a verification framework is that it can greatly enlarge the training data size and enable us to train a more accurate and reliable model in an end-to-end manner. In addition, to further improve the classification performance, we introduce the path signature (PS) features, which can capture the dynamic longitudinal information of the brain development for the ASD Identification. Experiments showed that our proposed method reaches the best result, i.e., 87% accuracy, 83% sensitivity and 90% specificity comparing to the state-of-the-art methods.

Type: Proceedings paper
Title: Siamese Verification Framework for Autism Identification During Infancy Using Cortical Path Signature Features
Event: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)
ISBN-13: 9781538693308
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/ISBI45749.2020.9098385
Publisher version: https://doi.org/10.1109/ISBI45749.2020.9098385
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Autism, Cortical Features, Verification Model, Path Signature
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Mathematics
URI: https://discovery.ucl.ac.uk/id/eprint/10102593
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