研究医生看真实与AI生成医学影像时眼动差异，揭示诊断注意力变化。
Shifts in Doctors' Eye Movements Between Real and AI-Generated Medical Images
- 通过眼动追踪分析注视点分布与扫视模式，量化注意力分配。
- 发现医生在看假影像时首次注视位置更偏向异常区域。
- 适用于医疗AI可信度评估与放射科医生训练优化。
眼动追踪在医学影像分析中至关重要，可揭示放射科医生视觉解读与诊断的规律。本文首先通过测量扫视方向、幅度及其联合分布等眼动模式，分析医生注意力与诊断一致性。进一步探究医生在观看真实（Real）与深度学习生成（Fake）图像时眼动行为是否发生改变。通过分析首次、末次、短时与最长时间注视点的注视偏移图，结合详细扫视模式，量化真实与合成图像间的眼动分布与视觉显著性差异。
原文摘要 · Abstract (English)
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.
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