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医学影像学和临床信息学中的深度学习和卷积神经网络-Deep Learning and Convolutional Neural Networks for Medical Imaging and Cli

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标题(title):Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics
医学影像学和临床信息学中的深度学习和卷积神经网络
作者(author):Le Lu, Xiaosong Wang, Gustavo Carneiro, Lin Yang
出版社(publisher):Springer International Publishing
大小(size):20 MB (21018100 bytes)
格式(extension):pdf
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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.





Table of contents :
Front Matter ....Pages i-xi
Front Matter ....Pages 1-1
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
Deep Learning for Muscle Pathology Image Analysis (Yuanpu Xie, Fujun Liu, Fuyong Xing, Lin Yang)....Pages 23-41
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
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
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
Front Matter ....Pages 117-117
Glaucoma Detection Based on Deep Learning Network in Fundus Image (Huazhu Fu, Jun Cheng, Yanwu Xu, Jiang Liu)....Pages 119-137
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
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
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
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
Front Matter ....Pages 217-217
Deep Hashing and Its Application for Histopathology Image Analysis (Xiaoshuang Shi, Lin Yang)....Pages 219-237
Tumor Growth Prediction Using Convolutional Networks (Ling Zhang, Lu Le, Ronald M. Summers, Electron Kebebew, Jianhua Yao)....Pages 239-260
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
Generative Low-Dose CT Image Denoising (Qingsong Yang, Pingkun Yan, Yanbo Zhang, Hengyong Yu, Yongyi Shi, Xuanqin Mou et al.)....Pages 277-297
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
Agent-Based Methods for Medical Image Registration (Shun Miao, Rui Liao)....Pages 323-345
Deep Learning for Functional Brain Connectivity: Are We There Yet? (Harish RaviPrakash, Arjun Watane, Sachin Jambawalikar, Ulas Bagci)....Pages 347-365
Front Matter ....Pages 367-367
ChestX-ray: Hospital-Scale Chest X-ray Database and Benchmarks on Weakly Supervised Classification and Localization of Common Thorax Diseases (Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, Ronald M. Summers)....Pages 369-392
Automatic Classification and Reporting of Multiple Common Thorax Diseases Using Chest Radiographs (Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Ronald M. Summers)....Pages 393-412
Deep Lesion Graph in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-Scale Lesion Database (Ke Yan, Xiaosong Wang, Le Lu, Ling Zhang, Adam P. Harrison, Mohammadhadi Bagheri et al.)....Pages 413-435
Simultaneous Super-Resolution and Cross-Modality Synthesis in Magnetic Resonance Imaging (Yawen Huang, Ling Shao, Alejandro F. Frangi)....Pages 437-457
Back Matter ....Pages 459-461
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