Shifts in Doctors' Eye Movements Between Real and AI-Generated Medical Images
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912343824269312 |
|---|---|
| author | 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 |
| author_facet | 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 |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_15007 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Shifts in Doctors' Eye Movements Between Real and AI-Generated Medical Images 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 Computer Vision and Pattern Recognition Human-Computer Interaction 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. |
| title | Shifts in Doctors' Eye Movements Between Real and AI-Generated Medical Images |
| topic | Computer Vision and Pattern Recognition Human-Computer Interaction |
| url | https://arxiv.org/abs/2504.15007 |