Deep Learning For Medical Image Analysis by S. Kevin ZhouDeep Learning For Medical Image Analysis by S. Kevin Zhou

Deep Learning For Medical Image Analysis

byS. Kevin ZhouEditorHayit Greenspan, Dinggang Shen

Paperback | January 30, 2017

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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
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...
Title:Deep Learning For Medical Image AnalysisFormat:PaperbackDimensions:458 pages, 8.75 × 6.35 × 0.68 inPublished:January 30, 2017Publisher:Academic PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0128104082

ISBN - 13:9780128104088


Table of Contents


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)


    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)


        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)


        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)


        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)