Deep Learning for Medical Image Analysis (ePub)
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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.
- Covers common research problems in medical image analysis and their challenges
- Describes 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 Chest X-ray, breast CAD, lung and chest, microscopy and pathology, etc.
- Includes a Foreword written by Nicholas Ayache
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.
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.
- 2017, 458 Seiten, Englisch
- Herausgegeben: S. Kevin Zhou, Hayit Greenspan, Dinggang Shen
- Verlag: Elsevier Science & Techn.
- ISBN-10: 0128104090
- ISBN-13: 9780128104095
- Erscheinungsdatum: 18.01.2017
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
- Dateiformat: ePub
- Größe: 51 MB
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