Deep learning in medical image analysis : challenges and applications / Gobert Lee, Hiroshi Fujita, editors.
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 top...
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| Other Authors: | , |
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| Format: | Ebook |
| Language: | English |
| Published: |
Cham :
Springer,
2020.
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| Series: | Advances in experimental medicine and biology ;
v. 1213. |
| Subjects: | |
| Online Access: | Springer eBooks |
| Summary: | 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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| Item Description: | Includes index. |
| Physical Description: | 1 online resource (184 pages). |
| ISBN: | 303033127X 9783030331276 3030331288 9783030331283 |