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Intelligent Systems Reference Library

Deep Learners and Deep Learner Descriptors for Medical Applications

Editors: Nanni, L., Brahnam, S., Brattin, R., Ghidoni, S., Jain, L.C. (Eds.)

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  • Presents recent research on all aspects of machine learning and data mining for health care
  • Focuses on general algorithms that can handle multiple sources of complex data in medical research databases
  • Includes various successful machine learning algorithms for health care as well as applications and descriptions of actual systems
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eBook 117,69 €
price for Spain (gross)
  • ISBN 978-3-030-42750-4
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Hardcover 145,59 €
price for Spain (gross)
  • ISBN 978-3-030-42748-1
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About this book

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. 

Table of contents (10 chapters)

Table of contents (10 chapters)
  • An Introduction to Deep Learners and Deep Learner Descriptors for Medical Applications

    Pages 1-5

    Nanni, Loris (et al.)

  • Feature Learning to Automatically Assess Radiographic Knee Osteoarthritis Severity

    Pages 9-93

    Antony, Joseph (et al.)

  • Classification of Tissue Regions in Histopathological Images: Comparison Between Pre-trained Convolutional Neural Networks and Local Binary Patterns Variants

    Pages 95-115

    Kather, Jakob N. (et al.)

  • Ensemble of Handcrafted and Deep Learned Features for Cervical Cell Classification

    Pages 117-135

    Nanni, Loris (et al.)

  • Deep Unsupervised Representation Learning for Audio-Based Medical Applications

    Pages 137-164

    Amiriparian, Shahin (et al.)

Buy this book

eBook 117,69 €
price for Spain (gross)
  • ISBN 978-3-030-42750-4
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 145,59 €
price for Spain (gross)
  • ISBN 978-3-030-42748-1
  • Free shipping for individuals worldwide
  • Immediate ebook access, if available*, with your print order
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
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Bibliographic Information

Bibliographic Information
Book Title
Deep Learners and Deep Learner Descriptors for Medical Applications
Editors
  • Loris Nanni
  • Sheryl Brahnam
  • Rick Brattin
  • Stefano Ghidoni
  • Lakhmi C. Jain
Series Title
Intelligent Systems Reference Library
Series Volume
186
Copyright
2020
Publisher
Springer International Publishing
Copyright Holder
Springer Nature Switzerland AG
eBook ISBN
978-3-030-42750-4
DOI
10.1007/978-3-030-42750-4
Hardcover ISBN
978-3-030-42748-1
Series ISSN
1868-4394
Edition Number
1
Number of Pages
XI, 284
Number of Illustrations
59 b/w illustrations, 51 illustrations in colour
Topics

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