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.

早期诊断阿尔茨海默病对于随后的医疗治疗非常重要，而眼球在特殊视觉刺激下的运动可能作为一种潜在的非侵入性生物标志物用于检测阿尔茨海默病患者的认知异常。我们提出了一种基于深度感应显著性对比网络 (DISCN) 的眼球运动分析方法，该方法可用于阿尔茨海默病的诊断。在 DISCN 中，显著注意模块使用分层显著注意机制 (SAA) 将正常眼球运动与视觉刺激的 RGB 和深度图像融合，以评估综合感兴趣图，该图像包含了来自视觉刺激和正常眼球运动行为的信息。此外，我们引入了串行注意模块 (SEA)，以强调最异常的眼球运动行为，以减少个人偏见，得到更加稳健的结果。根据我们的实验，DISCN 在分类阿尔茨海默病患者和正常对照组之间的眼球运动方面具有一致的有效性。