Fr. 134.00

Deep Learners and Deep Learner Descriptors for Medical Applications

English · Hardback

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Description

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This book introduces readers to the current trends in using deep learners and deep learner descriptors for medical applications. It reviews the recent literature and presents a variety of medical image and sound applications to illustrate the five major ways deep learners can be utilized: 1) by training a deep learner from scratch (chapters provide tips for handling imbalances and other problems with the medical data); 2) by implementing transfer learning from a pre-trained deep learner and extracting deep features for different CNN layers that can be fed into simpler classifiers, such as the support vector machine; 3) by fine-tuning one or more pre-trained deep learners on an unrelated dataset so that they are able to identify novel medical datasets; 4) by fusing different deep learner architectures; and 5) by combining the above methods to generate a variety of more elaborate ensembles. This book is a value resource for anyone involved in engineering deep learners for medical applications as well as to those interested in learning more about the current techniques in this exciting field. A number of chapters provide source code that can be used to investigate topics further or to kick-start new projects. 

List of contents

An Introduction to Deep Learners and Deep Leaner Descriptors for Medical Applications.- Feature Learning to Automatically Assess Radiographic Knee Osteoarthritis Severity. 

Product details

Assisted by Shery Brahnam (Editor), Sheryl Brahnam (Editor), Rick Brattin (Editor), Rick Brattin et al (Editor), Lakhmi C Jain (Editor), Stefano Ghidoni (Editor), Lakhmi C Jain (Editor), Lakhmi C. Jain (Editor), Lori Nanni (Editor), Loris Nanni (Editor)
Publisher Springer, Berlin
 
Languages English
Product format Hardback
Released 01.07.2020
 
EAN 9783030427481
ISBN 978-3-0-3042748-1
No. of pages 284
Dimensions 158 mm x 241 mm x 22 mm
Weight 596 g
Illustrations XI, 284 p. 110 illus., 51 illus. in color.
Series Intelligent Systems Reference Library
Subject Natural sciences, medicine, IT, technology > Technology > General, dictionaries

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