摘要

Abstract: With the prevalence of deep neural networks, machine intelligence has recently demonstrated performance comparable with, and in some cases superior to, that of human experts in medical imaging and computer assisted intervention. Such accomplishments can be largely credited to the ever-increasing computing power, as well as a growing abundance of medical data. As larger clusters of faster computing nodes become available at lower cost and in smaller form factors, more data can be used to train deeper neural networks with more layers and neurons, which usually translate to higher performance and at the same time higher computational complexity. For example, the widely used 3D U-Net for medical image segmentation has more than 16 million parameters and needs about 4.7×1013 floating point operations to process a 512×512×200 3D image. The large sizes and high computation complexity of neural networks have brought about various issues that need to be addressed by the joint efforts between hardware designers and medical practitioners towards hardware aware learning. In this talk, I will present novel solutions for the data acquisition and data processing stages in medical image computing respectively, using hardware-oriented schemes for lower latency, memory footprint and higher performance in embedded platforms. I will discuss how our hardware-aware machine learning approaches led to the real-time MRI segmentation for prosthetic valve implantation assistance, and enabled the world’s first AI assisted telementoring of cardiac surgery on April 3, 2019.

个人介绍

Bio: Dr. Yiyu Shi is currently a professor in the Department of Computer Science and Engineering at the University of Notre Dame, the site director of National Science Foundation I/UCRC Alternative and Sustainable Intelligent Computing, and the director of the Sustainable Computing Lab (SCL). He is also a visiting scientist at Boston Children’s Hospital, the primary pediatric program of Harvard Medical School. He received his B.S. in Electronic Engineering from Tsinghua University, Beijing, China in 2005, the M.S and Ph.D. degree in Electrical Engineering from the University of California, Los Angeles in 2007 and 2009 respectively. His current research interests focus on hardware intelligence and biomedical applications. In recognition of his research, more than a dozen of his papers have been nominated for or awarded as the best paper in top journals and conferences, including the 2021 IEEE Transactions on Computer-Aided Design Donald O Pederson Best Paper Award. He is also the recipient of Facebook Research Award, IBM Invention Achievement Award, Japan Society for the Promotion of Science (JSPS) Faculty Invitation Fellowship, Humboldt Research Fellowship, IEEE St. Louis Section Outstanding Educator Award, Academy of Science (St. Louis) Innovation Award, Missouri S&T Faculty Excellence Award, NSF CAREER Award, IEEE Region 5 Outstanding Individual Achievement Award, the Air Force Summer Faculty Fellowship, and IEEE Computer Society Mid-Career Research Achievement Award. He has served on the technical program committee of many international conferences. He is the deputy editor-in-chief of IEEE VLSI CAS Newsletter, and an associate editor of various IEEE and ACM journals. He is an IEEE CEDA distinguished lecturer and an ACM distinguished speaker.