bibtext: @InProceedings{ Liu_GEM_MICCAI2024,
                   author = { Liu, Shaonan and Chen, Wenting and Liu, Jie and Luo, Xiaoling and Shen, Linlin },
                   title = { { GEM: Context-Aware Gaze EstiMation with Visual Search Behavior Matching for Chest Radiograph } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15001 },
                   month = {October},
                   pages = { pending },
                }
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 enhance the interpretability of models in the field of medical imaging. https://github.com/Tiger-SN/GEM
title: GEM: Context-Aware Gaze EstiMation with Visual Search Behavior Matching for Chest Radiograph
authors: Liu, Shaonan Chen, Wenting Liu, Jie Luo, Xiaoling Shen, Linlin