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Deep Learning in Medical Image Analysis

Challenges and Applications

  • Book
  • © 2020

Overview

Editors:
  1. Gobert Lee

    1. College of Sciences & Engineering, Flinders University, Adelaide, Australia

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  2. Hiroshi Fujita

    1. Faculty of Engineering, Gifu University, Gifu, Japan

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    You can also search for this editor in PubMed   Google Scholar

  • Highlights issues and challenges of deep learning, specifically in medical imaging problems, surveying and discussing practical approaches in general and in the context of specific problems
  • Describes cutting-edge research and application 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
  • Provides insights in employing deep learning models for different medical tasks and scenarios as well as exploiting these novel approaches in emerging areas of research

Part of the book series: Advances in Experimental Medicine and Biology (AEMB, volume 1213)

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Table of contents (11 chapters)

  1. Front Matter

    Pages i-viii
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  2. Overview and Issues

    1. Front Matter

      Pages 1-1
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    2. Deep Learning in Medical Image Analysis

      • Heang-Ping Chan, Ravi K. Samala, Lubomir M. Hadjiiski, Chuan Zhou
      Pages 3-21

    3. Medical Image Synthesis via Deep Learning

      • Biting Yu, Yan Wang, Lei Wang, Dinggang Shen, Luping Zhou
      Pages 23-44

  3. Applications: Screening and Diagnosis

    1. Front Matter

      Pages 45-45
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    2. Deep Learning for Pulmonary Image Analysis: Classification, Detection, and Segmentation

      • Shoji Kido, Yasushi Hirano, Shingo Mabu
      Pages 47-58

    3. Deep Learning Computer-Aided Diagnosis for Breast Lesion in Digital Mammogram

      • Mugahed A. Al-antari, Mohammed A. Al-masni, Tae-Seong Kim
      Pages 59-72

    4. Decision Support System for Lung Cancer Using PET/CT and Microscopic Images

      • Atsushi Teramoto, Ayumi Yamada, Tetsuya Tsukamoto, Kazuyoshi Imaizumi, Hiroshi Toyama, Kuniaki Saito et al.
      Pages 73-94

    5. Lesion Image Synthesis Using DCGANs for Metastatic Liver Cancer Detection

      • Keisuke Doman, Takaaki Konishi, Yoshito Mekada
      Pages 95-106

    6. Retinopathy Analysis Based on Deep Convolution Neural Network

      • Yuji Hatanaka
      Pages 107-120

    7. Diagnosis of Glaucoma on Retinal Fundus Images Using Deep Learning: Detection of Nerve Fiber Layer Defect and Optic Disc Analysis

      • Chisako Muramatsu
      Pages 121-132

  4. Applications: Emerging Opportunities

    1. Front Matter

      Pages 133-133
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    2. Automatic Segmentation of Multiple Organs on 3D CT Images by Using Deep Learning Approaches

      • Xiangrong Zhou
      Pages 135-147

    3. Techniques and Applications in Skin OCT Analysis

      • Ai Ping Yow, Ruchir Srivastava, Jun Cheng, Annan Li, Jiang Liu, Leopold Schmetterer et al.
      Pages 149-163

    4. Deep Learning Technique for Musculoskeletal Analysis

      • Naoki Kamiya
      Pages 165-176

  5. Back Matter

    Pages 177-181
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Keywords

About this book

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.

Editors and Affiliations

  • College of Sciences & Engineering, Flinders University, Adelaide, Australia

    Gobert Lee

  • Faculty of Engineering, Gifu University, Gifu, Japan

    Hiroshi Fujita

About the editors

Gobert Lee is a lecturer in Statistical Science and the Director of Studies in Mathematics and Statistics at the College of Science and Engineering, and a research member of the Medical Device Research Institute, Flinders University, Adelaide, Australia. Gobert’s research interests include statistical pattern recognition, medical image segmentation, computer-aided-diagnosis systems, breast cancer detection and analysis, multi-organ CT segmentation and human voxel model generation.



Hiroshi Fujita is a Research Professor/Emeritus Professor of Gifu University. He is a member of the Society for Medical Image Information (president), the Research Group on Medical Imaging (adviser), the Japan Society for Medical Image Engineering (director), and some other societies. His research interests include computer-aided diagnosis system, image analysis and processing, and image evaluation in medicine. He has published over 1000 papers in Journals, Proceedings,Book chapters and Scientific Magazines.

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