  Early diagnosis of Alzheimer's Disease (AD) is very important for following
medical treatments, and eye movements under special visual stimuli may serve as
a potential non-invasive biomarker for detecting cognitive abnormalities of AD
patients. In this paper, we propose an Depth-induced saliency comparison
network (DISCN) for eye movement analysis, which may be used for diagnosis the
Alzheimers disease. In DISCN, a salient attention module fuses normal eye
movements with RGB and depth maps of visual stimuli using hierarchical salient
attention (SAA) to evaluate comprehensive saliency maps, which contain
information from both visual stimuli and normal eye movement behaviors. In
addition, we introduce serial attention module (SEA) to emphasis the most
abnormal eye movement behaviors to reduce personal bias for a more robust
result. According to our experiments, the DISCN achieves consistent validity in
classifying the eye movements between the AD patients and normal controls.
