Deep Learning and Convolutional Neural Networks for Medical Image Computing

1. Front Matter

   Pages i-xiii

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2. Review

   1. Front Matter

      Pages 1-1

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

      • Ronald M. Summers

      Pages 3-10

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

      • Gustavo Carneiro, Yefeng Zheng, Fuyong Xing, Lin Yang

      Pages 11-32

3. Detection and Localization

   1. Front Matter

      Pages 33-33

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

      • Holger R. Roth, Le Lu, Jiamin Liu, Jianhua Yao, Ari Seff, Kevin Cherry et al.

      Pages 35-48

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

      • Yefeng Zheng, David Liu, Bogdan Georgescu, Hien Nguyen, Dorin Comaniciu

      Pages 49-61

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

      • Fujun Liu, Lin Yang

      Pages 63-72

   5. Deep Learning for Histopathological Image Analysis: Towards Computerized Diagnosis on Cancers

      • Jun Xu, Chao Zhou, Bing Lang, Qingshan Liu

      Pages 73-95

   6. Interstitial Lung Diseases via Deep Convolutional Neural Networks: Segmentation Label Propagation, Unordered Pooling and Cross-Dataset Learning

      • Mingchen Gao, Ziyue Xu, Daniel J. Mollura

      Pages 97-111

   7. Three Aspects on Using Convolutional Neural Networks for Computer-Aided Detection in Medical Imaging

      • Hoo-Chang Shin, Holger R. Roth, Mingchen Gao, Le Lu, Ziyue Xu, Isabella Nogues et al.

      Pages 113-136

   8. Cell Detection with Deep Learning Accelerated by Sparse Kernel

      • Junzhou Huang, Zheng Xu

      Pages 137-157

   9. Fully Convolutional Networks in Medical Imaging: Applications to Image Enhancement and Recognition

      • Christian F. Baumgartner, Ozan Oktay, Daniel Rueckert

      Pages 159-179

   10. On the Necessity of Fine-Tuned Convolutional Neural Networks for Medical Imaging

       • 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

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   2. Fully Automated Segmentation Using Distance Regularised Level Set and Deep-Structured Learning and Inference

      • Tuan Anh Ngo, Gustavo Carneiro

      Pages 197-224

   3. Combining Deep Learning and Structured Prediction for Segmenting Masses in Mammograms

      • Neeraj Dhungel, Gustavo Carneiro, Andrew P. Bradley

      Pages 225-240

   4. Deep Learning Based Automatic Segmentation of Pathological Kidney in CT: Local Versus Global Image Context

      • Yefeng Zheng, David Liu, Bogdan Georgescu, Daguang Xu, Dorin Comaniciu

      Pages 241-255

   5. Robust Cell Detection and Segmentation in Histopathological Images Using Sparse Reconstruction and Stacked Denoising Autoencoders

      • Hai Su, Fuyong Xing, Xiangfei Kong, Yuanpu Xie, Shaoting Zhang, Lin Yang

      Pages 257-278

   6. Automatic Pancreas Segmentation Using Coarse-to-Fine Superpixel Labeling

      • Amal Farag, Le Lu, Holger R. Roth, Jiamin Liu, Evrim Turkbey, Ronald M. Summers

      Pages 279-302

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.

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

“This book … is very suitable for students, researchers and practitioner. In addition, the book provides an important and useful reference for experienced researchers on particular aspects of deep learning based medical image analysis.” (Guang Yang, IAPR Newsletter, Vol. 41 (2), April, 2019)

• NIH Clinical Center, Bethesda, USA

  Le Lu

• Siemens Healthcare Technology Center, Princeton, USA

  Yefeng Zheng

• University of Adelaide, Adelaide, Australia

  Gustavo Carneiro

• University of Florida, Gainesville, USA

  Lin Yang

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.

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