GazeSearch: Radiology Findings Search Benchmark

Medical eye-tracking data is an important information source for understanding how radiologists visually inter-pret 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 eye-tracking 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 re-fining 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. Code is available at https://github.com/UARK-AICV/GazeSearch.