GEM: Context-Aware Gaze EstiMation with Visual Search Behavior Matching for Chest Radiograph

• URL: http://arxiv.org/abs/2408.05502v1
• Date: Sat, 10 Aug 2024 09:46:25 GMT
• Title: GEM: Context-Aware Gaze EstiMation with Visual Search Behavior Matching for Chest Radiograph
• Authors: Shaonan Liu, Wenting Chen, Jie Liu, Xiaoling Luo, Linlin Shen,
• Abstract summary: We propose a context-aware Gaze EstiMation (GEM) network that utilizes eye gaze data collected from radiologists to simulate their visual search behavior patterns. It consists of a context-awareness module, visual behavior graph construction, and visual behavior matching. Experiments on four publicly available datasets demonstrate the superiority of GEM over existing methods.
• Score: 32.1234295417225
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Gaze estimation is pivotal in human scene comprehension tasks, particularly in medical diagnostic analysis. Eye-tracking technology facilitates the recording of physicians' ocular movements during image interpretation, thereby elucidating their visual attention patterns and information-processing strategies. In this paper, we initially define the context-aware gaze estimation problem in medical radiology report settings. To understand the attention allocation and cognitive behavior of radiologists during the medical image interpretation process, we propose a context-aware Gaze EstiMation (GEM) network that utilizes eye gaze data collected from radiologists to simulate their visual search behavior patterns throughout the image interpretation process. It consists of a context-awareness module, visual behavior graph construction, and visual behavior matching. Within the context-awareness module, we achieve intricate multimodal registration by establishing connections between medical reports and images. Subsequently, for a more accurate simulation of genuine visual search behavior patterns, we introduce a visual behavior graph structure, capturing such behavior through high-order relationships (edges) between gaze points (nodes). To maintain the authenticity of visual behavior, we devise a visual behavior-matching approach, adjusting the high-order relationships between them by matching the graph constructed from real and estimated gaze points. Extensive experiments on four publicly available datasets demonstrate the superiority of GEM over existing methods and its strong generalizability, which also provides a new direction for the effective utilization of diverse modalities in medical image interpretation and enhances the interpretability of models in the field of medical imaging. https://github.com/Tiger-SN/GEM

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