Deep Learning in Medical Image Analysis: Challenges and Applications: 1213 (Advances in Experimental Medicine and Biology, 1213) - Hardcover

Book 475 of 544: Advances in Experimental Medicine and Biology
 
9783030331276: Deep Learning in Medical Image Analysis: Challenges and Applications: 1213 (Advances in Experimental Medicine and Biology, 1213)
Hardcover
ISBN 10: 303033127X ISBN 13: 9783030331276
Publisher: Springer, 2020

Synopsis

This book presents cutting-edge research and applications of deep learning in a broad range of medical imaging scenarios, such as computer-aided diagnosis, image segmentation, tissue recognition and classification, and other areas of medical and healthcare problems. Each of its chapters covers a topic in depth, ranging from medical image synthesis and techniques for muskuloskeletal analysis to diagnostic tools for breast lesions on digital mammograms and glaucoma on retinal fundus images. It also provides an overview of deep learning in medical image analysis and highlights issues and challenges encountered by researchers and clinicians, surveying and discussing practical approaches in general and in the context of specific problems. Academics, clinical and industry researchers, as well as young researchers and graduate students in medical imaging, computer-aided-diagnosis, biomedical engineering and computer vision will find this book a great reference and very useful learning resource.

"synopsis" may belong to another edition of this title.

About the Author

Gobert Lee is a lecturer in Statistical Science and the Director of Studies in Mathematics and Statistics at the College of Science and Engineering, and a research member of the Medical Device Research Institute, Flinders University, Adelaide, Australia. Gobert’s research interests include statistical pattern recognition, medical image segmentation, computer-aided-diagnosis systems, breast cancer detection and analysis, multi-organ CT segmentation and human voxel model generation.

Hiroshi Fujita is a Research Professor/Emeritus Professor of Gifu University. He is a member of the Society for Medical Image Information (president), the Research Group on Medical Imaging (adviser), the Japan Society for Medical Image Engineering (director), and some other societies. His research interests include computer-aided diagnosis system, image analysis and processing, and image evaluation in medicine. He has published over 1000 papers in Journals, Proceedings,Book chapters and Scientific Magazines.

From the Back Cover

This book presents cutting-edge research and applications of deep learning in a broad range of medical imaging scenarios, such as computer-aided diagnosis, image segmentation, tissue recognition and classification, and other areas of medical and healthcare problems. Each of its chapters covers a topic in depth, ranging from medical image synthesis and techniques for muskuloskeletal analysis to diagnostic tools for breast lesions on digital mammograms and glaucoma on retinal fundus images. It also provides an overview of deep learning in medical image analysis and highlights issues and challenges encountered by researchers and clinicians, surveying and discussing practical approaches in general and in the context of specific problems. Academics, clinical and industry researchers, as well as young researchers and graduate students in medical imaging, computer-aided-diagnosis, biomedical engineering and computer vision will find this book a great reference and very useful learning resource.

"About this title" may belong to another edition of this title.

Publisher
Springer
Publication date
2020
Language
English
ISBN 10 
303033127X
ISBN 13 
9783030331276
Binding
Hardcover
Edition number
1
Number of pages
189
Editor
Lee Gobert, Fujita Hiroshi

Other Popular Editions of the Same Title

9783030331306: Deep Learning in Medical Image Analysis: Challenges and Applications: 1213 (Advances in Experimental Medicine and Biology, 1213)

Featured Edition

ISBN 10:  303033130X ISBN 13:  9783030331306
Publisher: Springer, 2021
Softcover