Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics

  • Le Lu
  • Xiaosong Wang
  • 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-xi
  2. Segmentation

    1. Front Matter
      Pages 1-1
    2. Yuanpu Xie, Fujun Liu, Fuyong Xing, Lin Yang
      Pages 23-41
    3. Yuyin Zhou, Qihang Yu, Yan Wang, Lingxi Xie, Wei Shen, Elliot K. Fishman et al.
      Pages 43-67
    4. Yingwei Li, Zhuotun Zhu, Yuyin Zhou, Yingda Xia, Wei Shen, Elliot K. Fishman et al.
      Pages 69-91
    5. Qi Dou, Cheng Chen, Cheng Ouyang, Hao Chen, Pheng Ann Heng
      Pages 93-115
  3. Detection and Localization

    1. Front Matter
      Pages 117-117
    2. Huazhu Fu, Jun Cheng, Yanwu Xu, Jiang Liu
      Pages 119-137
    3. Zhe Li, Chong Wang, Mei Han, Yuan Xue, Wei Wei, Li-Jia Li et al.
      Pages 139-161
    4. Gabriel Maicas, Andrew P. Bradley, Jacinto C. Nascimento, Ian Reid, Gustavo Carneiro
      Pages 163-178
    5. Siqi Liu, Daguang Xu, S. Kevin Zhou, Sasa Grbic, Weidong Cai, Dorin Comaniciu
      Pages 199-216
  4. Various Applications

    1. Front Matter
      Pages 217-217
    2. Ling Zhang, Lu Le, Ronald M. Summers, Electron Kebebew, Jianhua Yao
      Pages 239-260
    3. Yao Xiao, Skylar Stolte, Peng Liu, Yun Liang, Pina Sanelli, Ajay Gupta et al.
      Pages 261-275
    4. Qingsong Yang, Pingkun Yan, Yanbo Zhang, Hengyong Yu, Yongyi Shi, Xuanqin Mou et al.
      Pages 277-297
    5. Le Zhang, Marco Pereañez, Stefan K. Piechnik, Stefan Neubauer, Steffen E. Petersen, Alejandro F. Frangi
      Pages 299-321
    6. Shun Miao, Rui Liao
      Pages 323-345
    7. Harish RaviPrakash, Arjun Watane, Sachin Jambawalikar, Ulas Bagci
      Pages 347-365
  5. Large-Scale Data Mining and Data Synthesis

    1. Front Matter
      Pages 367-367
    2. Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, Ronald M. Summers
      Pages 369-392
    3. Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Ronald M. Summers
      Pages 393-412
    4. Ke Yan, Xiaosong Wang, Le Lu, Ling Zhang, Adam P. Harrison, Mohammadhadi Bagheri et al.
      Pages 413-435
    5. Yawen Huang, Ling Shao, Alejandro F. Frangi
      Pages 437-457
  6. Back Matter
    Pages 459-461

About this book


This book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ segmentation in medical image computing (radiological and pathological imaging modalities), and the construction and mining of large-scale radiology databases. It particularly focuses on the application of convolutional neural networks, and on recurrent neural networks like LSTM, using numerous practical examples to complement the theory. 

The book’s chief features are as follows: It highlights how deep neural networks can be used to address new questions and protocols, and to tackle current challenges in medical image computing; presents a comprehensive review of the latest research and literature; and describes a range of different methods that employ deep learning for object or landmark detection tasks in 2D and 3D medical imaging. In addition, the book examines a broad selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to text and image deep embedding for a large-scale chest x-ray image database; and discusses how deep learning relational graphs can be used to organize a sizable collection of radiology findings from real clinical practice, allowing semantic similarity-based retrieval.

The intended reader of this edited book is a professional engineer, scientist or a graduate student who is able to comprehend general concepts of image processing, computer vision and medical image analysis. They can apply computer science and mathematical principles into problem solving practices. It may be necessary to have a certain level of familiarity with a number of more advanced subjects: image formation and enhancement, image understanding, visual recognition in medical applications, statistical learning, deep neural networks, structured prediction and image segmentation.


Deep Learning Convolutional Neural Networks Medical Image Analytics Computer-Aided Diagnosis Hospital-Scale Imaging Data Process Disease Detection Organ Segmentation Medical Image Computing Radiology Database Construction and Mining Object and Landmark Detection 2D and 3D Medical Imaging Semantic Segmentation Text and Image Deep Embedding Learning Deep Relational Graphs Semantic Similarity-Based Retrieval

Editors and affiliations

  1. 1.Bethesda Research LabPAII Inc.BethesdaUSA
  2. 2.Nvidia CorporationBethesdaUSA
  3. 3.School of Computer ScienceUniversity of AdelaideAdelaideAustralia
  4. 4.Department of Biomedical EngineeringUniversity of FloridaGainesvilleUSA

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