Deep Learning for Medical Image Analysis
Published on 31. January 2017
Book
Paperback/Softback
458 pages
978-0-12-810408-8 (ISBN)
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.
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.
More details
Series
Language
English
Place of publication
San Diego
United States
Publishing group
Elsevier Science Publishing Co Inc
Target group
College/higher education
Professional and scholarly
Academic and industry researchers and graduate students in medical imaging, computer vision, and biomedical engineering.
Dimensions
Height: 235 mm
Width: 191 mm
Weight
970 gr
ISBN-13
978-0-12-810408-8 (9780128104088)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
New editions
S. Kevin Zhou | Hayit Greenspan | Dinggang Shen
Deep Learning for Medical Image Analysis
Book
11/2023
2nd Edition
Academic Press
€123.50
Shipment within 15-20 days
Additional editions
S. Kevin Zhou | Hayit Greenspan | Dinggang Shen
Deep Learning for Medical Image Analysis
E-Book
01/2017
Academic Press
€89.95
Available for download
Persons
S. Kevin Zhou, PhD is dedicated to research on medical image computing, especially analysis and reconstruction, and its applications in real practices. Currently, he is a Distinguished Professor and Founding Executive Dean of School of Biomedical Engineering, University of Science and Technology of China (USTC) and directs the Center for Medical Imaging, Robotics, Analytic Computing and Learning (MIRACLE). Dr. Zhou was a Principal Expert and a Senior R&D Director at Siemens Healthcare Research. He has been elected as a fellow of AIMBE, IAMBE, IEEE, MICCAI and NAI and serves the MICCAI society as a board member and treasurer.. Hayit Greenspan, PhD is focused on developing deep learning tools for medical image analysis, as well as their translation to the clinic. She is a Professor of Biomedical Engineering with the Faculty of Engineering at Tel-Aviv University (on Leave), and currently with the Department of Radiology and the AI and Human Health Department at the Icahn School of Medicine at Mount Sinai, NYC. She is the Director of the AI Core at the Biomedical Engineering and Imaging (BMEII) Institute and the Co-director of a new AI and emerging technologies PhD program at Mount Sinai. Dr. Greenspan is also a co-founder of RADLogics Inc., a startup company bringing AI tools to clinician support Dinggang Shen, PhD is a Professor and a Founding Dean with School of Biomedical Engineering, ShanghaiTech University, Shanghai, China, and also a Co-CEO of United Imaging Intelligence (UII), Shanghai. He is a Fellow of IEEE, AIMBE, IAPR and MICCAI. He was a Jeffrey Houpt Distinguished Investigator and a Full Professor (Tenured) with the University of North Carolina at Chapel Hill (UNC-CH), Chapel Hill, NC, USA. His research interests include medical image analysis, computer vision and pattern recognition. He has published more than 1,500 peer-reviewed papers in the international journals and conference proceedings, with H-index 130 and over 70K citations.
Editor
Principal Key Expert, Medical Image Analysis, Siemens Healthcare Technology Center, Princeton, New Jersey, USA
Head, Medical Image Processing and Analysis Lab, Biomedical Engineering Department, Faculty of Engineering, Tel-Aviv University, Israel
Professor, Department of Radiology and BRIC, UNC-Chapel Hill, USA
Content
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
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