Paper Info
Title
Predicting Autism Diagnosis using Image with Fixations and Synthetic Saccade Patterns
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 to a 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.
Year
DOI
Venue
2019
10.1109/ICMEW.2019.00125
2019 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)
Keywords
Field
DocType
Autism Spectrum Disorders, VisualSaliency, Deep Learning
Autism,Fixation (psychology),Pattern recognition,Convolutional neural network,Computer science,Artificial intelligence,Autism spectrum disorder,Deep learning,Classifier (linguistics),Saccade,Generative model
Conference
Volume
ISSN
ISBN
2019
2330-7927
978-1-5386-9215-8
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Chongruo Wu1283.39
Sidrah Liaqat200.68
Sen-Ching S. Cheung377670.97
Chen-Nee Chuah42006161.34
Sally Ozonoff500.68