Deep Learning in Medical Image Analysis: Challenges and Applications (1st ed. 2020) (Advances in Experimental Medicine and Biology #1213)

By: Hiroshi Fujita and Gobert Lee

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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.

Book Quality:: Publisher Quality
ISBN-13:: 9783030331283
Related ISBNs:: 9783030331276
Publisher:: Springer International Publishing
Date of Addition:: 05/30/20
Copyrighted By:: Springer
Adult content:: No
Language:: English
Has Image Descriptions:: No
Categories:: Nonfiction, Science, Computers and Internet, Technology, Medicine
Submitted By:: Bookshare Staff
Usage Restrictions:: This is a copyrighted book.
Edited by:: Hiroshi Fujita
Edited by:: Gobert Lee