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Deep Learning for Medical Image Analysis de S. Kevin Zhou

Deep Learning for Medical Image Analysis: The MICCAI Society book Series

Editat de S. Kevin Zhou, Hayit Greenspan, Dinggang Shen
en Limba Engleză Paperback – 31 ian 2017
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, includingChest X-ray, breast CAD, lung and chest, microscopy and pathology, etc.
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Specificații

ISBN-13: 9780128104088
ISBN-10: 0128104082
Pagini: 458
Dimensiuni: 191 x 235 x 34 mm
Greutate: 0.78 kg
Editura: ELSEVIER SCIENCE
Seria The MICCAI Society book Series

Public țintă

Academic and industry researchers and graduate students in medical imaging, computer vision, and biomedical engineering.

Cuprins

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

Descriere

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, includingChest X-ray, breast CAD, lung and chest, microscopy and pathology, etc.

"