Overview
- Highlights issues and challenges of deep learning, specifically in medical imaging problems, surveying and discussing practical approaches in general and in the context of specific problems
- Describes cutting-edge research and application 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
- Provides insights in employing deep learning models for different medical tasks and scenarios as well as exploiting these novel approaches in emerging areas of research
Part of the book series: Advances in Experimental Medicine and Biology (AEMB, volume 1213)
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About this book
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.
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Keywords
Table of contents (11 chapters)
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Front Matter
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Overview and Issues
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Front Matter
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Applications: Screening and Diagnosis
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Front Matter
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Applications: Emerging Opportunities
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Front Matter
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Back Matter
Editors and Affiliations
About the editors
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.
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.
Bibliographic Information
Book Title: Deep Learning in Medical Image Analysis
Book Subtitle: Challenges and Applications
Editors: Gobert Lee, Hiroshi Fujita
Series Title: Advances in Experimental Medicine and Biology
DOI: https://doi.org/10.1007/978-3-030-33128-3
Publisher: Springer Cham
eBook Packages: Biomedical and Life Sciences, Biomedical and Life Sciences (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-33127-6Published: 07 February 2020
Softcover ISBN: 978-3-030-33130-6Published: 07 February 2021
eBook ISBN: 978-3-030-33128-3Published: 06 February 2020
Series ISSN: 0065-2598
Series E-ISSN: 2214-8019
Edition Number: 1
Number of Pages: VIII, 181
Number of Illustrations: 17 b/w illustrations, 114 illustrations in colour
Topics: Biomedical Engineering/Biotechnology, Biomedical Engineering and Bioengineering, Imaging / Radiology, Computational Biology/Bioinformatics