Predicting autism diagnosis using image with fixations and synthetic saccade patterns

Chongruo Wu, Sidrah Liaqat, Sen Ching Cheung, Chen Nee Chuah, Sally J Ozonoff

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Signs of autism spectrum disorder (ASD) emerge in the first year of life in many children, but diagnosis is typically made much later, at an average age of 4 years in the United States. Early intervention is highly effective for young children with ASD, but is typically reserved for children with a formal diagnosis, making accurate identification as early as possible imperative. A screening tool that could identify ASD risk during infancy offers the opportunity for intervention before the full set of symptoms is present. In this paper, we propose two machine learning methods, synthetic saccade approach and image based approach, to automatically classify ASD given the scanpath data from children on free viewing of natural images. The first approach uses a generative model of synthetic saccade patterns to represent the baseline scan-path from a typical non-ASD individual and combines it with the input scanpath as well as other auxiliary data as inputs toa deep learning classifier. The second approach adopts a more holistic image based approach by feeding the input image and a sequence of fixation maps into a state-of-the-art convolutional neural network. Our experiments indicate that we can get 65.41% accuracy on the validation dataset.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages647-650
Number of pages4
ISBN (Electronic)9781538692141
DOIs
StatePublished - Jul 1 2019
Event2019 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2019 - Shanghai, China
Duration: Jul 8 2019Jul 12 2019

Publication series

NameProceedings - 2019 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2019

Conference

Conference2019 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2019
CountryChina
CityShanghai
Period7/8/197/12/19

Keywords

  • Autism Spectrum Disorders
  • Deep Learning
  • VisualSaliency

ASJC Scopus subject areas

  • Media Technology
  • Computer Vision and Pattern Recognition

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  • Cite this

    Wu, C., Liaqat, S., Cheung, S. C., Chuah, C. N., & Ozonoff, S. J. (2019). Predicting autism diagnosis using image with fixations and synthetic saccade patterns. In Proceedings - 2019 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2019 (pp. 647-650). [8795004] (Proceedings - 2019 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMEW.2019.00125