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

1. Front Matter

   Pages i-xi

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2. Segmentation

   1. Front Matter

      Pages 1-1

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   2. Pancreas Segmentation in CT and MRI via Task-Specific Network Design and Recurrent Neural Contextual Learning

      • Jinzheng Cai, Le Lu, Fuyong Xing, Lin Yang

      Pages 3-21

   3. Deep Learning for Muscle Pathology Image Analysis

      • Yuanpu Xie, Fujun Liu, Fuyong Xing, Lin Yang

      Pages 23-41

   4. 2D-Based Coarse-to-Fine Approaches for Small Target Segmentation in Abdominal CT Scans

      • Yuyin Zhou, Qihang Yu, Yan Wang, Lingxi Xie, Wei Shen, Elliot K. Fishman et al.

      Pages 43-67

   5. Volumetric Medical Image Segmentation: A 3D Deep Coarse-to-Fine Framework and Its Adversarial Examples

      • Yingwei Li, Zhuotun Zhu, Yuyin Zhou, Yingda Xia, Wei Shen, Elliot K. Fishman et al.

      Pages 69-91

   6. Unsupervised Domain Adaptation of ConvNets for Medical Image Segmentation via Adversarial Learning

      • Qi Dou, Cheng Chen, Cheng Ouyang, Hao Chen, Pheng Ann Heng

      Pages 93-115

3. Detection and Localization

   1. Front Matter

      Pages 117-117

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   2. Glaucoma Detection Based on Deep Learning Network in Fundus Image

      • Huazhu Fu, Jun Cheng, Yanwu Xu, Jiang Liu

      Pages 119-137

   3. Thoracic Disease Identification and Localization with Limited Supervision

      • Zhe Li, Chong Wang, Mei Han, Yuan Xue, Wei Wei, Li-Jia Li et al.

      Pages 139-161

   4. Deep Reinforcement Learning for Detecting Breast Lesions from DCE-MRI

      • Gabriel Maicas, Andrew P. Bradley, Jacinto C. Nascimento, Ian Reid, Gustavo Carneiro

      Pages 163-178

   5. Automatic Vertebra Labeling in Large-Scale Medical Images Using Deep Image-to-Image Network with Message Passing and Sparsity Regularization

      • Dong Yang, Tao Xiong, Daguang Xu

      Pages 179-197

   6. Anisotropic Hybrid Network for Cross-Dimension Transferable Feature Learning in 3D Medical Images

      • 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

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   2. Deep Hashing and Its Application for Histopathology Image Analysis

      • Xiaoshuang Shi, Lin Yang

      Pages 219-237

   3. Tumor Growth Prediction Using Convolutional Networks

      • Ling Zhang, Lu Le, Ronald M. Summers, Electron Kebebew, Jianhua Yao

      Pages 239-260

   4. Deep Spatial-Temporal Convolutional Neural Networks for Medical Image Restoration

      • Yao Xiao, Skylar Stolte, Peng Liu, Yun Liang, Pina Sanelli, Ajay Gupta et al.

      Pages 261-275

   5. Generative Low-Dose CT Image Denoising

      • Qingsong Yang, Pingkun Yan, Yanbo Zhang, Hengyong Yu, Yongyi Shi, Xuanqin Mou et al.

      Pages 277-297

   6. Image Quality Assessment for Population Cardiac Magnetic Resonance Imaging

      • Le Zhang, Marco Pereañez, Stefan K. Piechnik, Stefan Neubauer, Steffen E. Petersen, Alejandro F. Frangi

      Pages 299-321

   7. Agent-Based Methods for Medical Image Registration

      • Shun Miao, Rui Liao

      Pages 323-345

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

• Bethesda Research Lab, PAII Inc., Bethesda, USA

  Le Lu

• Nvidia Corporation, Bethesda, USA

  Xiaosong Wang

• School of Computer Science, University of Adelaide, Adelaide, Australia

  Gustavo Carneiro

• Department of Biomedical Engineering, University of Florida, Gainesville, USA

  Lin Yang

Dr. Le Lu is the Director of Ping An Technology US Research Labs, and an adjunct faculty member at Johns Hopkins University, USA.

Dr. Xiaosong Wang is a Senior Applied Research Scientist at Nvidia Corp., USA.

Dr. Gustavo Carneiro is an Associate Professor at the University of Adelaide, Australia.

Dr. Lin Yang is an Associate Professor at the University of Florida, USA.

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