Year: 2025
Type: preprint
Abstract: Eye-tracking analysis plays a vital role in medical imaging, providing key insights into how radiologists visually interpret and diagnose clinical cases. In this work, we first analyze radiologists' attention and agreement by measuring the distribution of various eye-movement patterns, including saccades direction, amplitude, and their joint distribution. These metrics help uncover patterns in attention allocation and diagnostic strategies. Furthermore, we investigate whether and how doctors' gaze behavior shifts when viewing authentic (Real) versus deep-learning-generated (Fake) images. To achieve this, we examine fixation bias maps, focusing on first, last, short, and longest fixations independently, along with detailed saccades patterns, to quantify differences in gaze distribution and visual saliency between authentic and synthetic images.
Source: arXiv (Cornell University)
Authors: Wong, David C, Wang, Bin, Durak, Gorkem, Tliba, Marouane, Kerkouri, Mohamed Amine, Chetouani, Aladine, Cetin, Ahmet Enis, Topel, Cagdas, Gennaro, Nicolo, Vendrami, Camila, Trabzonlu, Tugce Agirlar, Rahsepar, Amir Ali, Perronne, Laetitia, Antalek, Matthew, Ozturk, Onural, Okur, Gokcan, Gordon, Andrew C., Pyrros, Ayis, Miller, Frank H, Borhani, Amir A, Savas, Hatice, Hart, Eric M., Krupinski, Elizabeth A, Bagci, Ulas
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6979340258
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