Deep Network Design for Medical Image Computing

Deep Network Design for Medical Image Computing: Principles and Applications covers a range of MIC tasks and discusses design principles of these tasks for deep learning approaches in medicine. These include skin disease classification, vertebrae identification and localization, cardiac ultrasound image segmentation, 2D/3D medical image registration for intervention, metal artifact reduction, sparse-view artifact reduction, etc. For each topic, the book provides a deep learning-based solution that takes into account the medical or biological aspect of the problem and how the solution addresses a variety of important questions surrounding architecture, the design of deep learning techniques, when to introduce adversarial learning, and more. This book will help graduate students and researchers develop a better understanding of the deep learning design principles for MIC and to apply them to their medical problems. - Explains design principles of deep learning techniques for MIC - Contains cutting-edge deep learning research on MIC - Covers a broad range of MIC tasks, including the classification, detection, segmentation, registration, reconstruction and synthesis of medical images

Dr. Haofu Liao is currently an applied scientist of the Rekognition & Video Analysis team at Amazon Web Services, Inc. He received his Ph.D. degree in Computer Science from the University of Rochester, Rochester, NY in 2019 under the supervision of Prof. Jiebo Luo. Prior to that, he received his M.S. degree in Electrical and Computer Engineering from Northeastern University, Boston, MA in 2015 and his B.E. degree from the Beijing University of Posts and Telecommunications, Beijing, China in 2012. His research interest is in the interdisciplinary field between artificial intelligence and medicine. In particular, his research focuses on deep medical image computing where he designs deep learning-based approaches that are tailored for medical imaging or medical image analysis problems. He has authored more than 20 peer-reviewed papers in medical image computing and computer vision venues, including CVPR, MICCAI, TMI, ICPR.