Deep semantic gaze embedding and scanpath comparison for expertise classification during OPT viewing
This work addresses the challenge of accurately modeling expertise behavior in eye movement analysis for domain-specific applications like dental diagnosis.
The paper tackled the problem of classifying expertise based on eye movements by incorporating task semantics into gaze scanpath comparison, achieving 93% accuracy in distinguishing expert from novice dentists viewing radiographs.
Modeling eye movement indicative of expertise behavior is decisive in user evaluation. However, it is indisputable that task semantics affect gaze behavior. We present a novel approach to gaze scanpath comparison that incorporates convolutional neural networks (CNN) to process scene information at the fixation level. Image patches linked to respective fixations are used as input for a CNN and the resulting feature vectors provide the temporal and spatial gaze information necessary for scanpath similarity comparison.We evaluated our proposed approach on gaze data from expert and novice dentists interpreting dental radiographs using a local alignment similarity score. Our approach was capable of distinguishing experts from novices with 93% accuracy while incorporating the image semantics. Moreover, our scanpath comparison using image patch features has the potential to incorporate task semantics from a variety of tasks