Deep Learning in Healthcare

Paradigms and Applications

  • Yen-Wei Chen
  • Lakhmi C. Jain

Part of the Intelligent Systems Reference Library book series (ISRL, volume 171)

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Fundamentals of Deep Learning in Healthcare

    1. Front Matter
      Pages 1-1
    2. María Inmaculada García Ocaña, Karen López-Linares Román, Nerea Lete Urzelai, Miguel Ángel González Ballester, Iván Macía Oliver
      Pages 3-16
    3. Karen López-Linares Román, María Inmaculada García Ocaña, Nerea Lete Urzelai, Miguel Ángel González Ballester, Iván Macía Oliver
      Pages 17-31
    4. Weibin Wang, Dong Liang, Qingqing Chen, Yutaro Iwamoto, Xian-Hua Han, Qiaowei Zhang et al.
      Pages 33-51
    5. Yinhao Li, Yutaro Iwamoto, Yen-Wei Chen
      Pages 53-76
  3. Advanced Deep Learning in Healthcare

    1. Front Matter
      Pages 77-77
    2. Saeed Mohagheghi, Amir Hossein Foruzan, Yen-Wei Chen
      Pages 79-94
    3. Wenzhe Wang, Ruiwei Feng, Xuechen Liu, Yifei Lu, Yanjie Wang, Ruoqian Guo et al.
      Pages 95-110
    4. Aiga Suzuki, Hidenori Sakanashi, Shoji Kido, Hayaru Shouno
      Pages 111-126
    5. Liying Peng, Lanfen Lin, Hongjie Hu, Qiaowei Zhang, Huali Li, Qingqing Chen et al.
      Pages 149-164
    6. Shingo Mabu, Shoji Kido, Yasuhi Hirano, Takashi Kuremoto
      Pages 165-179
  4. Application of Deep Learning in Healthcare

    1. Front Matter
      Pages 201-201
  5. Back Matter
    Pages 217-218

About this book


This book provides a comprehensive overview of deep learning (DL) in medical and healthcare applications, including the fundamentals and current advances in medical image analysis, state-of-the-art DL methods for medical image analysis and real-world, deep learning-based clinical computer-aided diagnosis systems.

Deep learning (DL) is one of the key techniques of artificial intelligence (AI) and today plays an important role in numerous academic and industrial areas. DL involves using a neural network with many layers (deep structure) between input and output, and its main advantage of is that it can automatically learn data-driven, highly representative and hierarchical features and perform feature extraction and classification on one network. DL can be used to model or simulate an intelligent system or process using annotated training data.

Recently, DL has become widely used in medical applications, such as anatomic modelling, tumour detection, disease classification, computer-aided diagnosis and surgical planning. This book is intended for computer science and engineering students and researchers, medical professionals and anyone interested using DL techniques.


Deep Learning Machine Learning Medical Image Analysis Segmentation Classification Detection Computer-aided Diagnosis Artificial Intelligence

Editors and affiliations

  • Yen-Wei Chen
    • 1
  • Lakhmi C. Jain
    • 2
  1. 1.College of Information Science and EngineeringRitsumeikan UniversityKusatsuJapan
  2. 2.Faculty of Engineering and Information Technology, Centre for Artificial IntelligenceUniversity of TechnologySydneyAustralia

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