Description
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.- Covers common research problems in medical image analysis and their challenges- Describes deep learning methods and the theories behind approaches for medical image analysis- Teaches how algorithms are applied to a broad range of application areas, including Chest X-ray, breast CAD, lung and chest, microscopy and pathology, etc.- Includes a Foreword written by Nicholas Ayache
Table of Contents
PART 1: INTRODUCTION1. An introduction to neural network and deep learning (covering CNN, RNN, RBM, Autoencoders)Heung-Il Suk2. An Introduction to Deep Convolutional Neural Nets for Computer VisionSuraj Srinivas, Ravi K. Sarvadevabhatla, Konda R. Mopuri, Nikita Prabhu, Srinivas S.S. Kruthiventi and R. Venkatesh BabuPART 2: MEDICAL IMAGE DETECTION AND RECOGNITION3. Efficient Medical Image ParsingFlorin C. Ghesu, Bogdan Georgescu and Joachim Hornegger4. Multi-Instance Multi-Stage Deep Learning for Medical Image RecognitionZhennan Yan, Yiqiang Zhan, Shaoting Zhang, Dimitris Metaxas and Xiang Sean Zhou5. Automatic Interpretation of Carotid Intima–Media Thickness Videos Using Convolutional Neural Networks Nima Tajbakhsh, Jae Y. Shin, R. Todd Hurst, Christopher B. Kendall and Jianming Liang6. Deep Cascaded Networks for Sparsely Distributed Object Detection from Medical ImagesHao Chen, Qi Dou, Lequan Yu, Jing Qin, Lei Zhao, Vincent C.T. Mok, Defeng Wang, Lin Shi and Pheng-Ann Heng7. Deep Voting and Structured Regression for Microscopy Image AnalysisYuanpu Xie, Fuyong Xing and Lin YangPART 3 MEDICAL IMAGE SEGMENTATION8. Deep Learning Tissue Segmentation in Cardiac Histopathology ImagesJeffrey J. Nirschl, Andrew Janowczyk, Eliot G. Peyster, Renee Frank, Kenneth B. Margulies, Michael D. Feldman and Anant Madabhushi9. Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch MatchingYanrong Guo, Yaozong Gao and Dinggang Shen10. Characterization of Errors in Deep Learning-Based Brain MRI SegmentationAkshay Pai, Yuan-Ching Teng, Joseph Blair, Michiel Kallenberg, Erik B. Dam, Stefan Sommer, Christian Igel and Mads NielsenPART 4 MEDICAL IMAGE REGISTRATION11. Scalable High Performance Image Registration Framework by Unsupervised Deep Feature Representations LearningShaoyu Wang, Minjeong Kim, Guorong Wu and Dinggang Shen12. Convolutional Neural Networks for Robust and Real-Time 2-D/3-D RegistrationShun Miao, Jane Z. Wang and Rui LiaoPART 5 COMPUTER-AIDED DIAGNOSIS AND DISEASE QUANTIFICATION13. Chest Radiograph Pathology Categorization via Transfer LearningIdit Diamant, Yaniv Bar, Ofer Geva, Lior Wolf, Gali Zimmerman, Sivan Lieberman, Eli Konen and Hayit Greenspan14. Deep Learning Models for Classifying Mammogram Exams Containing Unregistered Multi-View Images and Segmentation Maps of LesionsGustavo Carneiro, Jacinto Nascimento and Andrew P. Bradley15. Randomized Deep Learning Methods for Clinical Trial Enrichment and Design in Alzheimer's DiseaseVamsi K. Ithapu, Vikas Singh and Sterling C. Johnson16. Deep Networks and Mutual Information Maximization for Cross-Modal Medical Image SynthesisRaviteja Vemulapalli, Hien Van Nguyen and S.K. Zhou17. Natural Language Processing for Large-Scale Medical Image Analysis Using Deep LearningHoo-Chang Shin, Le Lu and Ronald M. Summers
