Deep Learning for Medical Image Analysis (The MICCAI Society book Series) - Softcover

Softcover

ISBN 10: 032385124X ISBN 13: 9780323851244

Publisher: Academic Press, 2023

Synopsis

Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for academic and industry researchers and graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Deep learning provides exciting solutions for medical image analysis problems and is a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component are applied to medical image detection, segmentation, registration, and computer-aided analysis.

• Covers common research problems in medical image analysis and their challenges
• Describes the latest deep learning methods and the theories behind approaches for medical image analysis
• Teaches how algorithms are applied to a broad range of application areas including cardiac, neural and functional, colonoscopy, OCTA applications and model assessment· Includes a Foreword written by Nicholas Ayache

About this title

About the Authors

S. Kevin Zhou, PhD is dedicated to research on medical image computing, especially analysis and reconstruction, and its applications in real practices. Currently, he is a Distinguished Professor and Founding Executive Dean of School of Biomedical Engineering, University of Science and Technology of China (USTC) and directs the Center for Medical Imaging, Robotics, Analytic Computing and Learning (MIRACLE). Dr. Zhou was a Principal Expert and a Senior R&D Director at Siemens Healthcare Research. He has been elected as a fellow of AIMBE, IAMBE, IEEE, MICCAI and NAI and serves the MICCAI society as a board member and treasurer..

Hayit Greenspan, PhD is focused on developing deep learning tools for medical image analysis, as well as their translation to the clinic. She is a Professor of Biomedical Engineering with the Faculty of Engineering at Tel-Aviv University (on Leave), and currently with the Department of Radiology and the AI and Human Health Department at the Icahn School of Medicine at Mount Sinai, NYC. She is the Director of the AI Core at the Biomedical Engineering and Imaging (BMEII) Institute and the Co-director of a new AI and emerging technologies PhD program at Mount Sinai. Dr. Greenspan is also a co-founder of RADLogics Inc., a startup company bringing AI tools to clinician support

Dinggang Shen, PhD is a Professor and a Founding Dean with School of Biomedical Engineering, ShanghaiTech University, Shanghai, China, and also a Co-CEO of United Imaging Intelligence (UII), Shanghai. He is a Fellow of IEEE, AIMBE, IAPR and MICCAI. He was a Jeffrey Houpt Distinguished Investigator and a Full Professor (Tenured) with the University of North Carolina at Chapel Hill (UNC-CH), Chapel Hill, NC, USA. His research interests include medical image analysis, computer vision and pattern recognition. He has published more than 1,500 peer-reviewed papers in the international journals and conference proceedings, with H-index 130 and over 70K citations.

From the Back Cover

Deep learning provides exciting solutions for medical image analysis problems and is seen as a key method for future applications. This edition gives a clear understanding of the latest principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component have been applied to medical image reconstruction and synthesis, segmentation and registration and neural analysis, using a wide variety of application areas.

Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for academic and industry researchers in medical imaging analysis, and for graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis.

Edition details

• Publisher: Academic Press
• Publication date: 2023
• ISBN 10: 032385124X
• ISBN 13: 9780323851244
• Binding: Paperback
• Language: English
• Edition number: 2
• Number of pages: 518
• Editor: Zhou S. Kevin, Greenspan Hayit, Shen Dinggang