A Collaborative Computer Aided Diagnosis (C-CAD) System with Eye-Tracking, Sparse Attentional Model, and Deep Learning
Ulas Bagci
January 20, 2021, Wednesday, 3:00 PM - 4:00 PM EDT
Abstract
Vision researchers have been analyzing behaviors of radiologists during screening to understand how and why they miss tumors or misdiagnose. In this regard, eye-trackers have been instrumental in understanding the visual search processes of radiologists. However, most relevant studies in this aspect are not compatible with realistic radiology reading rooms. In this talk, I will share our unique experience for developing a paradigm-shifting computer-aided diagnosis (CAD) system, called collaborative CAD (C-CAD), that unifies CAD and eye-tracking systems in realistic radiology room settings. In other words, we are creating artificial intelligence (AI) tools that get benefits from human cognition and improve over complementary powers of AI and human intelligence. We first developed an eye-tracking interface providing radiologists with a real radiology reading room experience. Second, we proposed a novel computer algorithm that unifies eye-tracking data and a CAD system. The proposed C-CAD collaborates with radiologists via eye-tracking technology and helps them to improve their diagnostic decisions. The proposed C-CAD system has been tested in a lung and prostate cancer screening experiment with multiple radiologists. More recently, we also experimented with brain tumor segmentation with the proposed technology leading to promising results. In the last part of my talk, I will describe how to develop AI algorithms that are trusted by clinicians, namely "explainable AI algorithms". By embedding explainability into black-box nature of deep learning algorithms, it will be possible to deploy AI tools into clinical workflow and leading into more intelligent and less artificial algorithms available in radiology rooms.
Bio
Prof. Bagci is a faculty member at the Center for Research in Computer Vision (CRCV), and the Assistant Professor in University of Central Florida (UCF). His research interests are Artificial intelligence, machine learning and their applications in biomedical and clinical imaging. Previously, he was a staff scientist and the lab co-manager at the NIH's Center for Infectious Disease Imaging (CIDI) Lab, department of Radiology and Imaging Sciences (RAD&IS). At NIH, Prof. Bagci has developed and implemented educational and scientific research initiatives, and mentored postdoctoral and postbaccalaureate fellows for quantitative image analysis in clinical and pre-clinical projects at the Clinical Center. Prof. Bagci had also been the leading scientist (image analyst) in biosafety/bioterrorism project initiated jointly by NIAID and IRF.

Prof. Bagci obtained his PhD degree from School of Computer Science, University of Nottingham (UK) in collaboration with Radiology department of University of Pennsylvania (with Prof. Udupa, MIPG). He has masters from Electrical Engineering and Computer Sciences and certificates of mastery from statistics, public health, and clinical trials. Prof. Bagci is senior member of IEEE and RSNA, and member of scientific organizations such as Society of Nuclear Medicine and Molecular Imaging (SNMMI), American Statistical Association (ASA), Royal Statistical Society (RSS), AAAS, and MICCAI. He has served as a program committee member for various conferences, and a regular reviewer for many prestigious journals in his fields and received best reviewer awards (most recently MICCAI 2016 Best Scientific Reviewer Award). Prof. Bagci is the recipient of many awards including NIH's FARE award (twice), RSNA Merit Certificates (5+ times), best paper awards, poster prizes, and several highlights in journal covers, media, and news. Prof. Bagci was co-chair of Image Processing Track of SPIE Medical Imaging Conference, 2017, and technical committee member of MICCAI 2018.