Deep Learning and Convolutional Neural Networks for Medical Image Computing

Precision Medicine, High Performance and Large-Scale Datasets

  • Le Lu
  • Yefeng Zheng
  • Gustavo Carneiro
  • Lin Yang

Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Review

  3. Detection and Localization

    1. Front Matter
      Pages 33-33
    2. Holger R. Roth, Le Lu, Jiamin Liu, Jianhua Yao, Ari Seff, Kevin Cherry et al.
      Pages 35-48
    3. Yefeng Zheng, David Liu, Bogdan Georgescu, Hien Nguyen, Dorin Comaniciu
      Pages 49-61
    4. Hoo-Chang Shin, Holger R. Roth, Mingchen Gao, Le Lu, Ziyue Xu, Isabella Nogues et al.
      Pages 113-136
    5. Christian F. Baumgartner, Ozan Oktay, Daniel Rueckert
      Pages 159-179
    6. Nima Tajbakhsh, Jae Y. Shin, Suryakanth R. Gurudu, R. Todd Hurst, Christopher B. Kendall, Michael B. Gotway et al.
      Pages 181-193
  4. Segmentation

    1. Front Matter
      Pages 195-195
    2. Neeraj Dhungel, Gustavo Carneiro, Andrew P. Bradley
      Pages 225-240
    3. Yefeng Zheng, David Liu, Bogdan Georgescu, Daguang Xu, Dorin Comaniciu
      Pages 241-255
    4. Hai Su, Fuyong Xing, Xiangfei Kong, Yuanpu Xie, Shaoting Zhang, Lin Yang
      Pages 257-278
    5. Amal Farag, Le Lu, Holger R. Roth, Jiamin Liu, Evrim Turkbey, Ronald M. Summers
      Pages 279-302

About this book

Introduction

This timely text/reference 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.

Topics and 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 and views of Dr. Ronald M. Summers in medical imaging-based computer-aided diagnosis and its interaction with deep learning
  • Presents a comprehensive review of the latest research and literature on deep learning for medical image analysis
  • 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 for automated image interpretation

This pioneering volume will prove invaluable to researchers and graduate students wishing to employ deep neural network models and representations for medical image analysis and medical imaging applications.

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.

Keywords

Deep Learning Convolutional Neural Networks Medical Image Analytics Computer-Aided Diagnosis Hospital-Scale Imaging Data Process

Editors and affiliations

  1. 1.NIH Clinical CenterBethesdaUSA
  2. 2.Siemens Healthcare Technology CenterPrincetonUSA
  3. 3.University of AdelaideAdelaideAustralia
  4. 4.University of FloridaGainesvilleUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-42999-1
  • Copyright Information Springer International Publishing Switzerland 2017
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-42998-4
  • Online ISBN 978-3-319-42999-1
  • Series Print ISSN 2191-6586
  • Series Online ISSN 2191-6594
  • About this book
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