  Eye tracking research is important in computer vision because it can help us
understand how humans interact with the visual world. Specifically for
high-risk applications, such as in medical imaging, eye tracking can help us to
comprehend how radiologists and other medical professionals search, analyze,
and interpret images for diagnostic and clinical purposes. Hence, the
application of eye tracking techniques in disease classification has become
increasingly popular in recent years. Contemporary works usually transform gaze
information collected by eye tracking devices into visual attention maps (VAMs)
to supervise the learning process. However, this is a time-consuming
preprocessing step, which stops us from applying eye tracking to radiologists'
daily work. To solve this problem, we propose a novel gaze-guided graph neural
network (GNN), GazeGNN, to leverage raw eye-gaze data without being converted
into VAMs. In GazeGNN, to directly integrate eye gaze into image
classification, we create a unified representation graph that models both
images and gaze pattern information. With this benefit, we develop a real-time,
real-world, end-to-end disease classification algorithm for the first time in
the literature. This achievement demonstrates the practicality and feasibility
of integrating real-time eye tracking techniques into the daily work of
radiologists. 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. The code is available at
https://github.com/ukaukaaaa/GazeGNN.
