Description: 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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EAN: 9783030331276
UPC: 9783030331276
ISBN: 9783030331276
MPN: N/A
Recommended Age Range: 0-12 months
Book Title: Deep Learning in Medical Image Analysis: Challenge
Item Length: 25.4 cm
Number of Pages: 181 Pages
Language: English
Publication Name: Deep Learning in Medical Image Analysis: Challenges and Applications
Publisher: Springer Nature Switzerland Ag
Publication Year: 2020
Subject: Engineering & Technology, Radiology
Item Height: 254 mm
Item Weight: 602 g
Type: Textbook
Author: Hiroshi Fujita, Gobert Lee
Item Width: 178 mm
Format: Hardcover
