Upcoming Talks

Radiologist-centered AI with Eye Tracking Techniques

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.

Bin Wang is a second-year PhD student in the Department of Electrical and Computer Engineering at Northwestern University under the supervision of Prof. Ulas Bagci. He is mainly working on developing Human-centered AI for medical image analysis. His research interests include Eye Tracking, Multi-Modal Foundational Models, and Human-Computer Interaction. He has over ten papers published in leading machine learning conferences and journals, including MICCAI, WACV, ICASSP, CVPR and NeurIPS workshop, and Medical Image Analysis. (Personal Website: ukaukaaaa.github.io)

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