Deep learning in medical image analysis : challenges and applications Authors:Gobert Lee, Hiroshi Fujita
©2020Description: 181 pages.Content type: text Media type: unmediated Carrier type: volumeISBN: 9783030331276.Genre/Form: Print books.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| Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|
| On Shelf | R859.7.A78 D44 2020 (Browse shelf) | Available | AU00000000019937 |
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| R859.7.A78 A78 2020 Artificial intelligence in precision health / | R859.7.A78 C54 2020 Machine learning in medicine - a complete overview / | R859.7.A78 C66 2019 ARTIFICIAL INTELLIGENCE IN MEDICINE. | R859.7.A78 D44 2020 Deep learning in medical image analysis : challenges and applications | R859.7 .A78 E83 2017 Strategies in biomedical data science : driving force for innovation / | R859.7.A78 G73 2022 The doctor and the algorithm : promise, peril, and the future of health ai / | R859.7 .C65 2018 Healthcare simulation education : evidence, theory & practice / |
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