Depth-induced Saliency Comparison Network for Diagnosis of Alzheimer's Disease via Jointly Analysis of Visual Stimuli and Eye Movements

TL;DR

This paper introduces DISCN, a novel neural network that combines visual stimuli and eye movement data with depth information to improve early Alzheimer's diagnosis through non-invasive analysis.

Contribution

The paper presents a depth-induced saliency comparison network with hierarchical and serial attention modules for more accurate eye movement analysis in AD diagnosis.

Findings

DISCN effectively classifies eye movements between AD patients and controls.

The model demonstrates consistent validity across experiments.

Depth and attention modules enhance diagnostic accuracy.

Abstract

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.