@inproceedings{f294c6726b094ea08f978040af06ebc7,
title = "Deep semantic gaze embedding and scanpath comparison for expertise classification during OPT viewing",
abstract = "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.",
keywords = "Deep Learning, Eye Tracking, Learning, Medical image interpretation, Scanpath analysis",
author = "Nora Castner and K{\"u}ebler, {Thomas C.} and Katharina Scheiter and Juliane Richter and Th{\'e}r{\'e}se Eder and Fabian H{\"u}ettig and Constanze Keutel and Enkelejda Kasneci",
note = "Publisher Copyright: {\textcopyright} 2020 ACM.; 2020 ACM Symposium on Eye Tracking Research and Applications, ETRA 2020 ; Conference date: 02-06-2020 Through 05-06-2020",
year = "2020",
month = feb,
day = "6",
doi = "10.1145/3379155.3391320",
language = "English",
series = "Eye Tracking Research and Applications Symposium (ETRA)",
publisher = "Association for Computing Machinery",
editor = "Spencer, {Stephen N.}",
booktitle = "Proceedings ETRA 2020 Full Papers - ACM Symposium on Eye Tracking Research and Applications",
}