The application of eye-tracking techniques in medical image analysis has
become increasingly popular in recent years. It collects the visual search
patterns of the domain experts, containing much important information about
health and disease. Therefore, how to efficiently integrate radiologists' gaze
patterns into the diagnostic analysis turns into a critical question. Existing
works usually transform gaze information into visual attention maps (VAMs) to
supervise the learning process. However, this time-consuming procedure makes it
difficult to develop end-to-end algorithms. In this work, we propose a novel
gaze-guided graph neural network (GNN), GazeGNN, to perform disease
classification from medical scans. In GazeGNN, we create a unified
representation graph that models both the image and gaze pattern information.
Hence, the eye-gaze information is directly utilized without being converted
into VAMs. With this benefit, we develop a real-time, real-world, end-to-end
disease classification algorithm for the first time and avoid the noise and
time consumption introduced during the VAM preparation. To our best knowledge,
GazeGNN is the first work that adopts GNN to integrate image and eye-gaze data.
Our experiments on the public chest X-ray dataset show that our proposed method
exhibits the best classification performance compared to existing methods.

本文中提出了一种新的注视引导的图神经网络（GazeGNN），用于对医学扫描进行疾病分类，该方法不需要将目光信息转换为视觉注意力图；该方法在公共胸部 X-ray 数据集上实验，相较于现有方法，本方法取得了最佳分类性能。