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 language | English |
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Title of host publication | Proceedings - 2019 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2019 |
Pages | 647-650 |
Number of pages | 4 |
ISBN (Electronic) | 9781538692141 |
DOIs | |
State | Published - Jul 2019 |
Event | 2019 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2019 - Shanghai, China Duration: Jul 8 2019 → Jul 12 2019 |
Publication series
Name | Proceedings - 2019 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2019 |
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Conference
Conference | 2019 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2019 |
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Country/Territory | China |
City | Shanghai |
Period | 7/8/19 → 7/12/19 |
Bibliographical note
Publisher Copyright:© 2019 IEEE.
Keywords
- Autism Spectrum Disorders
- Deep Learning
- VisualSaliency
ASJC Scopus subject areas
- Media Technology
- Computer Vision and Pattern Recognition