Existing research has shown the potential of classifying Alzheimers Disease
(AD) from eye-tracking (ET) data with classifiers that rely on task-specific
engineered features. In this paper, we investigate whether we can improve on
existing results by using a Deep-Learning classifier trained end-to-end on raw
ET data. This classifier (VTNet) uses a GRU and a CNN in parallel to leverage
both visual (V) and temporal (T) representations of ET data and was previously
used to detect user confusion while processing visual displays. A main
challenge in applying VTNet to our target AD classification task is that the
available ET data sequences are much longer than those used in the previous
confusion detection task, pushing the limits of what is manageable by
LSTM-based models. We discuss how we address this challenge and show that VTNet
outperforms the state-of-the-art approaches in AD classification, providing
encouraging evidence on the generality of this model to make predictions from
ET data.

使用深度学习分类器（VTNet）对原始眼动数据进行端到端训练，以改进目标 AD 分类任务中的性能，并证明该模型在从眼动数据中进行预测方面具有普适性。