Congrats to Bin Wang for presentation in MedAI

Although artificial intelligence (AI) based computer-aided diagnosis systems have been shown to be useful in medical image analysis, current deep learning methods still suffer (1) challenging localization of lesions and (2) low-efficient clinical practice (3) lack of expert knowledge. 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 medical imaging, eye tracking can help us comprehend how radiologists and other medical professionals search, analyze, and interpret images for diagnostic and clinical purposes. In this study, we investigate how to apply eye tracking techniques to real clinical practice, which builds a time-efficient, robust, and radiologist-centered computer-aided diagnosis system.