Deep Learning in Medical Image Analysis - Challenges and Applications (Hardcover, 1st ed. 2020)

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.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Advances in Experimental Medicine and Biology, 1213
Release date:	February 2020
First published:	2020
Editors:	Gobert Lee • Hiroshi Fujita
Dimensions:	254 x 178mm (L x W)
Format:	Hardcover
Pages:	181
Edition:	1st ed. 2020
ISBN-13:	978-3-03-033127-6
Categories:	Books > Medicine > Other branches of medicine > Medical imaging > General Books > Medicine > Nursing & ancillary services > Biomedical engineering > General
LSN:	3-03-033127-X
Barcode:	9783030331276

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