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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 learni… Read more
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· 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
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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.