Deep Learning for Medical Image Analysis - Product Image

Deep Learning for Medical Image Analysis

  • ID: 3833451
  • Book
  • 458 Pages
  • Elsevier Science and Technology
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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
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PART 1: INTRODUCTION

1. An introduction to neural network and deep learning (covering CNN, RNN, RBM, Autoencoders) (Heung-Il Suk)

2. An Introduction to Deep Convolutional Neural Nets for Computer Vision  (Suraj Srinivas, Ravi K. Sarvadevabhatla, Konda R. Mopuri, Nikita Prabhu, Srinivas S.S. Kruthiventi and R. Venkatesh Babu)

PART 2: MEDICAL IMAGE DETECTION AND RECOGNITION

3. Efficient Medical Image Parsing (Florin C. Ghesu, Bogdan Georgescu and Joachim Hornegger)

4. Multi-Instance Multi-Stage Deep Learning for Medical Image Recognition (Zhennan Yan, Yiqiang Zhan, Shaoting Zhang, Dimitris Metaxas and Xiang Sean Zhou)

5. 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 Liang)

6. Deep Cascaded Networks for Sparsely Distributed Object Detection from Medical Images (Hao Chen, Qi Dou, Lequan Yu, Jing Qin, Lei Zhao, Vincent C.T. Mok, Defeng Wang, Lin Shi and Pheng-Ann Heng)

7. Deep Voting and Structured Regression for Microscopy Image Analysis (Yuanpu Xie, Fuyong Xing and Lin Yang)

PART 3 MEDICAL IMAGE SEGMENTATION

8. Deep Learning Tissue Segmentation in Cardiac Histopathology Images (Jeffrey J. Nirschl, Andrew Janowczyk, Eliot G. Peyster, Renee Frank, Kenneth B. Margulies, Michael D. Feldman and Anant Madabhushi)

9. Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching (Yanrong Guo, Yaozong Gao and Dinggang Shen)

10. Characterization of Errors in Deep Learning-Based Brain MRI Segmentation (Akshay Pai, Yuan-Ching Teng, Joseph Blair, Michiel Kallenberg, Erik B. Dam, Stefan Sommer, Christian Igel and Mads Nielsen)

PART 4 MEDICAL IMAGE REGISTRATION

11. Scalable High Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning (Shaoyu Wang, Minjeong Kim, Guorong Wu and Dinggang Shen)

12. Convolutional Neural Networks for Robust and Real-Time 2-D/3-D Registration (Shun Miao, Jane Z. Wang and Rui Liao)

PART 5 COMPUTER-AIDED DIAGNOSIS AND DISEASE QUANTIFICATION

13. Chest Radiograph Pathology Categorization via Transfer Learning (Idit Diamant, Yaniv Bar, Ofer Geva, Lior Wolf, Gali Zimmerman, Sivan Lieberman, Eli Konen and Hayit Greenspan)

14. Deep Learning Models for Classifying Mammogram Exams Containing Unregistered Multi-View Images and Segmentation Maps of Lesions (Gustavo Carneiro, Jacinto Nascimento and Andrew P. Bradley)

15. Randomized Deep Learning Methods for Clinical Trial Enrichment and Design in Alzheimer's Disease (Vamsi K. Ithapu, Vikas Singh and Sterling C. Johnson)

16. Deep Networks and Mutual Information Maximization for Cross-Modal Medical Image Synthesis (Raviteja Vemulapalli, Hien Van Nguyen and S.K. Zhou)

17. Natural Language Processing for Large-Scale Medical Image Analysis Using Deep Learning (Hoo-Chang Shin, Le Lu and Ronald M. Summers)

Index
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Zhou, S. Kevin
S. Kevin Zhou, Ph.D. is currently a Principal Key Expert Scientist at Siemens Healthcare Technology Center, leading a team of full time research scientists and students dedicated to researching and developing innovative solutions for medical and industrial imaging products. His research interests lie in computer vision and machine/deep learning and their applications to medical image analysis, face recognition and modeling, etc. He has published over 150 book chapters and peer-reviewed journal and conference papers, registered over 250 patents and inventions, written two research monographs, and edited three books. He has won multiple technology, patent and product awards, including R&D 100 Award and Siemens Inventor of the Year. He is an editorial board member for Medical Image Analysis journal and a fellow of American Institute of Medical and Biological Engineering (AIMBE).
Greenspan, Hayit
Hayit Greenspan is a Tenured Professor at the Biomedical Engineering Dept. Faculty of Engineering, Tel-Aviv University. She was a visiting Professor at the Radiology Dept. Stanford University, and is currently affiliated with the International Computer Science Institute (ICSI) at Berkeley. Dr. Greenspan's research focuses on image modeling and analysis, deep learning, and content-based image retrieval. Research projects include: Brain MRI research (structural and DTI), CT and X-ray image analysis - automated detection to segmentation and characterization. Dr. Greenspan has over 150 publications in leading international journals and conference proceedings. She has received several awards and is a coauthor on several patents. Currently her Lab is funded for Deep Learning in Medical Imaging by the INTEL Collaborative Research Institute for Computational Intelligence (ICRI-CI). Dr. Greenspan is a member of several journal and conference program committees, including SPIE medical imaging, IEEE_ISBI and MICCAI. She is an Associate Editor for the IEEE Trans on Medical Imaging (TMI) journal. Recently she was the Lead guest editor for an IEEE-TMI special Issue on "Deep Learning in Medical Imaging”, May 2016.
Shen, Dinggang
Dinggang Shen is a Professor of Radiology, Biomedical Research Imaging Center (BRIC), Computer Science, and Biomedical Engineering in the University of North Carolina at Chapel Hill (UNC-CH). He is currently directing the Center for Image Informatics and Analysis, the Image Display, Enhancement, and Analysis (IDEA) Lab in the Department of Radiology, and also the medical image analysis core in the BRIC. He was a tenure-track assistant professor in the University of Pennsylvanian (UPenn), and a faculty member in the Johns Hopkins University. Dr. Shen's research interests include medical image analysis, computer vision, and pattern recognition. He has published more than 700 papers in the international journals and conference proceedings. He serves as an editorial board member for six international journals. He has served in the Board of Directors, The Medical Image Computing and Computer Assisted Intervention (MICCAI) Society, in 2012-2015.
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