In this study, we developed a novel system, called Gaze2Segment, integrating biological and computer vision techniques to support radiologists’ reading experience with an automatic image segmentation task. During diagnostic assessment of lung CT scans, the radiologists’ gaze information were used to create a visual attention map. Next, this map was combined with a computer-derived saliency map, extracted from the gray-scale CT images. The visual attention map was used as an input for indicating roughly the location of a region of interest. With computer-derived saliency information, on the other hand, we aimed at finding foreground and background cues for the object of interest found in the previous step.
I've got my B.Sc. in Electrical Engineering from Amirkabir University of Technology.
I was a member of Real-Time lab and
I was fortunate to work with Prof. Heidar Ali Talebi as my undergraduate advisor.
I am curretly pursuing my PhD in Computer Science at the Center for Research in Computer Vision (CRCV) under Dr. Ulas Bagci at University of Central Florida (UCF).
My research is mainly focused on the intersection of machine learning and computer vision.
More specifically I am working on machine learning methods such as deep learning, reinforcement learning, generative models and adversial learning for tasks such as attention modeling, decision making, segmentation, detection etc.
My work is mainly focused on medical applications of such methods for development of Computer Aided Diagnosis (CAD) systems.
A short version of my CV can be found below.