Speaker: Ulas Bagci, PhD, Northwestern University
Date: Wednesday, February 15, 2023
Time: 10:00AM Central Time
Location: https://uwmadison.webex.com/uwmadison/j.php?MTID=ma20eab5835181e4628731547b83688fb
Title: “Trustworthy AI for Imaging-based Diagnoses”
Abstract: In this talk, I will focus on the failures of deep learning / AI algorithms and propose several approaches to increase the robustness of AI-powered medical imaging systems. Roadmaps to such trustworthy systems will be analyzed: 1) algorithmic robustness, 2) interpretable/explainable machine learning systems, and 3) human in-the-loop machine learning system. For each of these, I will give a layout. For algorithmic robustness, I will introduce a success story of deep network architecture, called capsule networks, and demonstrate its effectiveness and robustness compared to commonly used systems; hence, increasing its trustworthiness to be used in the high-risk applications. For human in the loop system, I will share our unique experience in 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. Last, but not least, I will introduce our new algorithm developed to better localize regions where the algorithm learns. Compared to commonly used Grad-Cam algorithms, we obtain superior performance when depicting salient regions that are most informative. COVID19 examples will be demonstrated as a recent hot topic. Lastly, I will discuss future directions that medical imaging physicians and scientists should think about when AI comes into play.
Bio: Ulas Bagci, Ph.D., is an Associate Professor at the Northwestern University’s Radiology Department in Chicago, and a courtesy professor at the Biomedical Engineering Dept., Electrical and Computer Engineering Dept. of Northwestern, as well as the Center for Research in Computer Vision (CRCV), department of computer science, University of Central Florida (UCF). His research interests are artificial intelligence, machine learning, and their applications in biomedical and clinical imaging. Dr. Bagci has more than 270 peer-reviewed articles on these topics. Previously, he was a staff scientist and lab co-manager at the National Institutes of Health’s radiology and imaging sciences department, center for infectious disease imaging. Dr. Bagci holds several NIH grants (R and U) (as Principal Investigator) and serves as a steering committee member of AIR (artificial intelligence resource) at the NIH. Dr. Bagci also serves as an area chair for MICCAI for several years and he is an associate editor of top-tier journals in his fields such as IEEE Trans. on Medical Imaging, Medical Physics, and Medical Image Analysis. Prof. Bagci teaches machine learning, advanced deep learning methods, computer and robot vision, and medical imaging courses. He has several international and national recognitions including best paper and reviewer awards.