Deep Learning for Medical Image Analysis

✍ Scribed by S. Kevin Zhou, Hayit Greenspan and Dinggang Shen (Eds.)

Publisher: Academic Press
Year: 2017
Tongue: English
Leaves: 424
Edition: 1st Edition
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

Deep learning is providing exciting solutions for medical image analysis problems and is seen as a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component have been applied to medical image detection, segmentation and registration, and computer-aided analysis, using a wide variety of application areas.

Deep Learning for Medical Image Analysis is a great learning resource for academic and industry researchers in medical imaging analysis, and for graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis.

✦ Table of Contents

Content:
Front Matter,Copyright,Contributors,About the Editors,ForewordEntitled to full textPart I: IntroductionChapter 1 - An Introduction to Neural Networks and Deep Learning, Pages 3-24, Heung-Il Suk
Chapter 2 - An Introduction to Deep Convolutional Neural Nets for Computer Vision, Pages 25-52, Suraj Srinivas, Ravi K. Sarvadevabhatla, Konda R. Mopuri, Nikita Prabhu, Srinivas S.S. Kruthiventi, R. Venkatesh Babu
Chapter 3 - Efficient Medical Image Parsing, Pages 55-81, Florin C. Ghesu, Bogdan Georgescu, Joachim Hornegger
Chapter 4 - Multi-Instance Multi-Stage Deep Learning for Medical Image Recognition, Pages 83-104, Zhennan Yan, Yiqiang Zhan, Shaoting Zhang, Dimitris Metaxas, Xiang Sean Zhou
Chapter 5 - Automatic Interpretation of Carotid Intima–Media Thickness Videos Using Convolutional Neural Networks, Pages 105-131, Nima Tajbakhsh, Jae Y. Shin, R. Todd Hurst, Christopher B. Kendall, Jianming Liang
Chapter 6 - Deep Cascaded Networks for Sparsely Distributed Object Detection from Medical Images, Pages 133-154, Hao Chen, Qi Dou, Lequan Yu, Jing Qin, Lei Zhao, Vincent C.T. Mok, Defeng Wang, Lin Shi, Pheng-Ann Heng
Chapter 7 - Deep Voting and Structured Regression for Microscopy Image Analysis, Pages 155-175, Yuanpu Xie, Fuyong Xing, Lin Yang
Chapter 8 - Deep Learning Tissue Segmentation in Cardiac Histopathology Images, Pages 179-195, Jeffrey J. Nirschl, Andrew Janowczyk, Eliot G. Peyster, Renee Frank, Kenneth B. Margulies, Michael D. Feldman, Anant Madabhushi
Chapter 9 - Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching, Pages 197-222, Yanrong Guo, Yaozong Gao, Dinggang Shen
Chapter 10 - Characterization of Errors in Deep Learning-Based Brain MRI Segmentation, Pages 223-242, Akshay Pai, Yuan-Ching Teng, Joseph Blair, Michiel Kallenberg, Erik B. Dam, Stefan Sommer, Christian Igel, Mads Nielsen
Chapter 11 - Scalable High Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning, Pages 245-269, Shaoyu Wang, Minjeong Kim, Guorong Wu, Dinggang Shen
Chapter 12 - Convolutional Neural Networks for Robust and Real-Time 2-D/3-D Registration, Pages 271-296, Shun Miao, Jane Z. Wang, Rui Liao
Chapter 13 - Chest Radiograph Pathology Categorization via Transfer Learning, Pages 299-320, Idit Diamant, Yaniv Bar, Ofer Geva, Lior Wolf, Gali Zimmerman, Sivan Lieberman, Eli Konen, Hayit Greenspan
Chapter 14 - Deep Learning Models for Classifying Mammogram Exams Containing Unregistered Multi-View Images and Segmentation Maps of Lesions1, Pages 321-339, Gustavo Carneiro, Jacinto Nascimento, Andrew P. Bradley
Chapter 15 - Randomized Deep Learning Methods for Clinical Trial Enrichment and Design in Alzheimer's Disease, Pages 341-378, Vamsi K. Ithapu, Vikas Singh, Sterling C. Johnson
Chapter 16 - Deep Networks and Mutual Information Maximization for Cross-Modal Medical Image Synthesis, Pages 381-403, Raviteja Vemulapalli, Hien Van Nguyen, S. Kevin Zhou
Chapter 17 - Natural Language Processing for Large-Scale Medical Image Analysis Using Deep Learning, Pages 405-421, Hoo-Chang Shin, Le Lu, Ronald M. Summers
Index, Pages 423-433

✦ Subjects

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