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Eye movements have been widely investigated to study the atypical visualattention in Autism Spectrum Disorder (ASD). The majority of these studies havebeen focused on limited eye movement features by statistical comparisonsbetween ASD and Typically Developing (TD) groups, which make it difficult toaccurately separate ASD from TD at the individual level. The deep learningtechnology has been highly successful in overcoming this issue by automaticallyextracting features important for classification through a data-driven learningprocess. However, there is still a lack of end-to-end deep learning frameworkfor recognition of abnormal attention in ASD. In this study, we developed anovel two-stream deep learning network for this recognition based on 700 imagesand corresponding eye movement patterns of ASD and TD, and obtained an accuracyof 0.95, which was higher than the previous state-of-the-art. We nextcharacterized contributions to the classification at the single image level andnon-linearly integration of this single image level information during theclassification. Moreover, we identified a group of pixel-level visual featureswithin these images with greater impacts on the classification. Together, thistwo-stream deep learning network provides us a novel and powerful tool torecognize and understand abnormal visual attention in ASD.

A Two-stream End-to-End Deep Learning Network for Recognizing Atypical Visual Attention in Autism Spectrum Disorder