Alzheimer's Disease (AD) causes a continuous decline in memory, thinking, and judgment. Traditional diagnoses are usually based on clinical experience, which is limited by some realistic factors. In this paper, we focus on exploiting deep learning techniques to diagnose AD based on eye-tracking behaviors. Visual attention, as typical eye-tracking behavior, is of great clinical value to detect cognitive abnormalities in AD patients. To better analyze the differences in visual attention between AD patients and normals, we first conduct a 3D comprehensive visual task on a non-invasive eye-tracking system to collect visual attention heatmaps. We then propose a multi-layered comparison convolution neural network (MC-CNN) to distinguish the visual attention differences between AD patients and normals. In MC-CNN, the multi-layered representations of heatmaps are obtained by hierarchical convolution to better encode eye-movement behaviors, which are further integrated into a distance vector to benefit the comprehensive visual task. Extensive experimental results on the collected dataset demonstrate that MC-CNN achieves consistent validity in classifying AD patients and normals with eye-tracking data.

翻译：阿尔茨海默氏氏病(AD)导致记忆、思维和判断的持续下降。 传统诊断通常以临床经验为基础,而临床经验受一些现实因素的限制。 在本文中,我们侧重于利用深层次的学习技术,根据眼睛跟踪行为来诊断AD。 视觉关注作为典型的目视跟踪行为,对于检测AD病人的认知异常具有极大的临床价值。 为了更好地分析AD病人和正常人之间视觉关注的差别,我们首先对非侵入性的眼睛跟踪系统进行三维综合视觉任务,以收集视觉关注热测图。 然后我们提出多层比较神经神经网络(MC-CNN),以区分AD病人和正常人之间的视觉关注差异。 在MC-CNN 中,通过分级演获得对热测图的多层次描述,以更好地将眼睛移动行为编码成一个远程矢量,从而有利于全面视觉任务。 所收集的数据集的广泛实验结果表明,MC-CNN在将AD病人和正常人与眼睛跟踪数据分类方面实现了一致性。</s>