Uncertainty Inspired Autism Spectrum Disorder Screening

Ying Zhang, Yaping Huang, Jiansong Qi, Sihui Zhang, Mei Tian, Yi Tian

Abstract: People with autism spectrum disorder (ASD) show distinguishing preferences for specific visual stimuli compared to typically developed (TD) individuals, opening the door for objective and quantitative screening by eye-tracking data analysis. However, existing eye-tracking-based ASD screening approaches often assume that there are no individual differences and that all stimuli contribute equally to the prediction of an ASD. Consequently, a fixed number of images are usually selected by a pre-defined strategy for further training and testing, ignoring the distinct characteristics of various subjects viewing the same image. To address the aforementioned difficulties, we propose a novel Uncertainty-inspired ASD Screening Network (UASN) that dynamically modifies the contribution of each stimulus viewed by different subjects. Specifically, we estimate the uncertainty of each stimulus by considering the variation between the subject’s fixation map and the ones of the two clinical groups (i.e., ASD and TD) and further utilize it for weighting the training loss. Besides, to reduce the diagnosis time, instead of the shuffle-appeared mode of image viewing, we propose an uncertainty-based personalized diagnosis method to dynamically rank the viewing images according to the preferences of different subjects, which can achieve high prediction accuracy with only a small set of images. Experiments demonstrate the superior performance of our proposed UASN.