Medical eye-tracking data is an important information source for understanding how radiologists visually interpret medical images. This information not only improves the accuracy of deep learning models for X-ray analysis but also their interpretability, enhancing transparency in decision-making. However, the current eye-tracking data is dispersed, unprocessed, and ambiguous, making it difficult to derive meaningful insights. Therefore, there is a need to create a new dataset with more focus and purposeful eyetracking data, improving its utility for diagnostic applications. In this work, we propose a refinement method inspired by the target-present visual search challenge: there is a specific finding and fixations are guided to locate it. After refining the existing eye-tracking datasets, we transform them into a curated visual search dataset, called GazeSearch, specifically for radiology findings, where each fixation sequence is purposefully aligned to the task of locating a particular finding. Subsequently, we introduce a scan path prediction baseline, called ChestSearch, specifically tailored to GazeSearch. Finally, we employ the newly introduced GazeSearch as a benchmark to evaluate the performance of current state-of-the-art methods, offering a comprehensive assessment for visual search in the medical imaging domain.

本研究解决了当前医学眼动追踪数据分散且模糊的问题，影响其在影像诊断中的应用。文章提出了一种新方法，创建了一个针对放射学发现的精细化眼动追踪数据集GazeSearch，目的是使每个注视序列与特定诊断目标对齐，并引入了新的评估基准，以推动医学影像领域的视觉搜索技术发展。研究结果可显著提高深度学习模型的准确性和可解释性，提升临床决策的透明度。

注视搜索：放射学发现搜索基准