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Advances in Computer Vision and Pattern Recognition

Deep Learning and Convolutional Neural Networks for Medical Image Computing

Precision Medicine, High Performance and Large-Scale Datasets

Editors: Lu, L., Zheng, Y., Carneiro, G., Yang, L. (Eds.)

  • Addresses the challenges of applying deep learning for medical image analysis
  • Presents insights from leading experts in the field
  • Describes principles and best practices
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eBook $99.00
price for USA (gross)
  • ISBN 978-3-319-42999-1
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $129.00
price for USA
  • ISBN 978-3-319-42998-4
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this book

This book presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks, with the theory supported by practical examples. Features: highlights how the use of deep neural networks can address new questions and protocols, as well as improve upon existing challenges in medical image computing; discusses the insightful research experience of Dr. Ronald M. Summers; presents a comprehensive review of the latest research and literature; describes a range of different methods that make use of deep learning for object or landmark detection tasks in 2D and 3D medical imaging; examines a varied selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to interleaved text and image deep mining on a large-scale radiology image database.

About the authors

Dr. Le Lu is a Staff Scientist in the Radiology and Imaging Sciences Department of the National Institutes of Health Clinical Center, Bethesda, MD, USA.
Dr. Yefeng Zheng is a Senior Staff Scientist at Siemens Healthcare Technology Center, Princeton, NJ, USA.
Dr. Gustavo Carneiro is an Associate Professor in the School of Computer Science at The University of Adelaide, Australia.
Dr. Lin Yang is an Associate Professor in the Department of Biomedical Engineering at the University of Florida, Gainesville, FL, USA.

Table of contents (17 chapters)

  • Deep Learning and Computer-Aided Diagnosis for Medical Image Processing: A Personal Perspective

    Summers, Ronald M.

    Pages 3-10

  • Review of Deep Learning Methods in Mammography, Cardiovascular, and Microscopy Image Analysis

    Carneiro, Gustavo (et al.)

    Pages 11-32

  • Efficient False Positive Reduction in Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation

    Roth, Holger R. (et al.)

    Pages 35-48

  • Robust Landmark Detection in Volumetric Data with Efficient 3D Deep Learning

    Zheng, Yefeng (et al.)

    Pages 49-61

  • A Novel Cell Detection Method Using Deep Convolutional Neural Network and Maximum-Weight Independent Set

    Liu, Fujun (et al.)

    Pages 63-72

Buy this book

eBook $99.00
price for USA (gross)
  • ISBN 978-3-319-42999-1
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $129.00
price for USA
  • ISBN 978-3-319-42998-4
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Deep Learning and Convolutional Neural Networks for Medical Image Computing
Book Subtitle
Precision Medicine, High Performance and Large-Scale Datasets
Editors
  • Le Lu
  • Yefeng Zheng
  • Gustavo Carneiro
  • Lin Yang
Series Title
Advances in Computer Vision and Pattern Recognition
Copyright
2017
Publisher
Springer International Publishing
Copyright Holder
Springer International Publishing Switzerland
eBook ISBN
978-3-319-42999-1
DOI
10.1007/978-3-319-42999-1
Hardcover ISBN
978-3-319-42998-4
Series ISSN
2191-6586
Edition Number
1
Number of Pages
XIII, 326
Number of Illustrations and Tables
17 b/w illustrations, 100 illustrations in colour
Topics