  Eye movements have been widely investigated to study the atypical visual
attention in Autism Spectrum Disorder (ASD). The majority of these studies have
been focused on limited eye movement features by statistical comparisons
between ASD and Typically Developing (TD) groups, which make it difficult to
accurately separate ASD from TD at the individual level. The deep learning
technology has been highly successful in overcoming this issue by automatically
extracting features important for classification through a data-driven learning
process. However, there is still a lack of end-to-end deep learning framework
for recognition of abnormal attention in ASD. In this study, we developed a
novel two-stream deep learning network for this recognition based on 700 images
and corresponding eye movement patterns of ASD and TD, and obtained an accuracy
of 0.95, which was higher than the previous state-of-the-art. We next
characterized contributions to the classification at the single image level and
non-linearly integration of this single image level information during the
classification. Moreover, we identified a group of pixel-level visual features
within these images with greater impacts on the classification. Together, this
two-stream deep learning network provides us a novel and powerful tool to
recognize and understand abnormal visual attention in ASD.
