Academic Press Deep Network Design for Medical Image Computing: Principles and Applications (The MICCAI Society book Series)

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. Review Helps readers deep learning design methods specifically developed to solve medical problems From the Back Cover The majority of current medical image computing (MIC) research into deep learning directly applies the techniques developed from computer vision. This book draws a distinction between computer vision and MIC and investigates what are the correct ways to leverage deep learning for medical problems.<br><br>This book deliberately selects topics that cover a broad range of MIC tasks and discusses the design principles of these tasks for specialized deep learning approaches to 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, it provides a deep learning-based solution that takes into account the medical or biological aspect of the problem, before discussing how the solution addresses the following questions:<br><br>1) what is a good network architecture for this medical application?<br>2) what are the ways to fuse medical knowledge into the design of deep learning techniques?<br>3) If, how, and when to introduce adversarial learning?<br><br>The approach of <i>Deep Network Design for Medical Image Computing: Principles and Applications</i> 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. About the Author 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. Professor S. Kevin Zhou obtained his PhD degree from University of Maryland, College Park. He is a Professor at Chinese Academy of Sciences. Prior to this, he was a Principal Expert and a Senior R&D director at Siemens Healthcare. Dr. Zhou has published 180+ book chapters and peer-reviewed journal and conference papers, registered 250+ patents and inventions, written two research monographs, and edited three books. His two most recent books are entitled "Medical Image Recognition, Segmentation and Pars