Deep Learning for Medical Image Analysis
Deep learning is providing exciting solutions for medical image analysis problems and is seen as 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 have been applied to medical image detection, segmentation and registration, and computer-aided analysis, using a wide variety of application areas. Deep Learning for Medical Image Analysis 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.
About the Author
S. Kevin Zhou, Ph.D. is currently a Principal Key Expert Scientist at Siemens Healthcare Technology Center, leading a team of full time research scientists and students dedicated to researching and developing innovative solutions for medical and industrial imaging products. His research interests lie in computer vision and machine/deep learning and their applications to medical image analysis, face recognition and modeling, etc. He has published over 150 book chapters and peer-reviewed journal and conference papers, registered over 250 patents and inventions, written two research monographs, and edited three books. He has won multiple technology, patent and product awards, including R&D 100 Award and Siemens Inventor of the Year. He is an editorial board member for Medical Image Analysis journal and a fellow of American Institute of Medical and Biological Engineering (AIMBE). Hayit Greenspan is a Tenured Professor at the Biomedical Engineering Dept. Faculty of Engineering, Tel-Aviv University. She was a visiting Professor at the Radiology Dept. Stanford University, and is currently affiliated with the International Computer Science Institute (ICSI) at Berkeley. Dr. Greenspan’s research focuses on image modeling and analysis, deep learning, and content-based image retrieval. Research projects include: Brain MRI research (structural and DTI), CT and X-ray image analysis – automated detection to segmentation and characterization. Dr. Greenspan has over 150 publications in leading international journals and conference proceedings. She has received several awards and is a coauthor on several patents. Currently her Lab is funded for Deep Learning in Medical Imaging by the INTEL Collaborative Research Institute for Computational Intelligence (ICRI-CI). Dr. Greenspan is a member of several journal and conference program committees, including SPIE medical imaging, IEEE_ISBI and MICCAI. She is an Associate Editor for the IEEE Trans on Medical Imaging (TMI) journal. Recently she was the Lead guest editor for an IEEE-TMI special Issue on “Deep Learning in Medical Imaging”, May 2016. Dinggang Shen is a Professor of Radiology, Biomedical Research Imaging Center (BRIC), Computer Science, and Biomedical Engineering in the University of North Carolina at Chapel Hill (UNC-CH). He is currently directing the Center for Image Informatics and Analysis, the Image Display, Enhancement, and Analysis (IDEA) Lab in the Department of Radiology, and also the medical image analysis core in the BRIC. He was a tenure-track assistant professor in the University of Pennsylvanian (UPenn), and a faculty member in the Johns Hopkins University. Dr. Shen’s research interests include medical image analysis, computer vision, and pattern recognition. He has published more than 700 papers in the international journals and conference proceedings. He serves as an editorial board member for six international journals. He has served in the Board of Directors, The Medical Image Computing and Computer Assisted Intervention (MICCAI) Society, in 2012-2015.
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