Deep Learning for Medical Image Analysis

Deep Learning for Medical Image Analysis

1st Edition - January 18, 2017

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  • Editors: S. Kevin Zhou, Hayit Greenspan, Dinggang Shen
  • Paperback ISBN: 9780128104088
  • eBook ISBN: 9780128104095

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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.

Key Features

  • 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


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

Table of Contents

  • Part I: Introduction

    1. An Introduction to Neural Networks and Deep Learning

    • Abstract
    • 1.1. Introduction
    • 1.2. Feed-Forward Neural Networks
    • 1.3. Convolutional Neural Networks
    • 1.4. Deep Models
    • 1.5. Tricks for Better Learning
    • 1.6. Open-Source Tools for Deep Learning
    • References

    2. An Introduction to Deep Convolutional Neural Nets for Computer Vision

    • Abstract
    • 2.1. Introduction
    • 2.2. Convolutional Neural Networks
    • 2.3. CNN Flavors
    • 2.4. Software for Deep Learning
    • References

    Part II: Medical Image Detection and Recognition

    3. Efficient Medical Image Parsing

    • Abstract
    • 3.1. Introduction
    • 3.2. Background and Motivation
    • 3.3. Methodology
    • 3.4. Experiments
    • 3.5. Conclusion
    • Disclaimer
    • References

    4. Multi-Instance Multi-Stage Deep Learning for Medical Image Recognition

    • Abstract
    • 4.1. Introduction
    • 4.2. Related Work
    • 4.3. Methodology
    • 4.4. Results
    • 4.5. Discussion and Future Work
    • References

    5. Automatic Interpretation of Carotid Intima–Media Thickness Videos Using Convolutional Neural Networks

    • Abstract
    • Acknowledgement
    • 5.1. Introduction
    • 5.2. Related Work
    • 5.3. CIMT Protocol
    • 5.4. Method
    • 5.5. Experiments
    • 5.6. Discussion
    • 5.7. Conclusion
    • References

    6. Deep Cascaded Networks for Sparsely Distributed Object Detection from Medical Images

    • Abstract
    • Acknowledgements
    • 6.1. Introduction
    • 6.2. Method
    • 6.3. Mitosis Detection from Histology Images
    • 6.4. Cerebral Microbleed Detection from MR Volumes
    • 6.5. Discussion and Conclusion
    • References

    7. Deep Voting and Structured Regression for Microscopy Image Analysis

    • Abstract
    • Acknowledgements
    • 7.1. Deep Voting: A Robust Approach Toward Nucleus Localization in Microscopy Images
    • 7.2. Structured Regression for Robust Cell Detection Using Convolutional Neural Network
    • References

    Part III: Medical Image Segmentation

    8. Deep Learning Tissue Segmentation in Cardiac Histopathology Images

    • Abstract
    • 8.1. Introduction
    • 8.2. Experimental Design and Implementation
    • 8.3. Results and Discussion
    • 8.4. Concluding Remarks
    • Notes
    • Disclosure Statement
    • Funding
    • References

    9. Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching

    • Abstract
    • 9.1. Background
    • 9.2. Proposed Method
    • 9.3. Experiments
    • 9.4. Conclusion
    • References

    10. Characterization of Errors in Deep Learning-Based Brain MRI Segmentation

    • Abstract
    • 10.1. Introduction
    • 10.2. Deep Learning for Segmentation
    • 10.3. Convolutional Neural Network Architecture
    • 10.4. Experiments
    • 10.5. Results
    • 10.6. Discussion
    • 10.7. Conclusion
    • References

    Part IV: Medical Image Registration

    11. Scalable High Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning

    • Abstract
    • 11.1. Introduction
    • 11.2. Proposed Method
    • 11.3. Experiments
    • 11.4. Conclusion
    • References

    12. Convolutional Neural Networks for Robust and Real-Time 2-D/3-D Registration

    • Abstract
    • 12.1. Introduction
    • 12.2. X-Ray Imaging Model
    • 12.3. Problem Formulation
    • 12.4. Regression Strategy
    • 12.5. Feature Extraction
    • 12.6. Convolutional Neural Network
    • 12.7. Experiments and Results
    • 12.8. Discussion
    • Disclaimer
    • References

    Part V: Computer-Aided Diagnosis and Disease Quantification

    13. Chest Radiograph Pathology Categorization via Transfer Learning

    • Abstract
    • Acknowledgements
    • 13.1. Introduction
    • 13.2. Image Representation Schemes with Classical (Non-Deep) Features
    • 13.3. Extracting Deep Features from a Pre-Trained CNN Model
    • 13.4. Extending the Representation Using Feature Fusion and Selection
    • 13.5. Experiments and Results
    • 13.6. Conclusion
    • References

    14. Deep Learning Models for Classifying Mammogram Exams Containing Unregistered Multi-View Images and Segmentation Maps of Lesions

    • Abstract
    • Acknowledgements
    • 14.1. Introduction
    • 14.2. Literature Review
    • 14.3. Methodology
    • 14.4. Materials and Methods
    • 14.5. Results
    • 14.6. Discussion
    • 14.7. Conclusion
    • References

    15. Randomized Deep Learning Methods for Clinical Trial Enrichment and Design in Alzheimer's Disease

    • Abstract
    • Acknowledgements
    • 15.1. Introduction
    • 15.2. Background
    • 15.3. Optimal Enrichment Criterion
    • 15.4. Randomized Deep Networks
    • 15.5. Experiments
    • 15.6. Discussion
    • References

    Part VI: Others

    16. Deep Networks and Mutual Information Maximization for Cross-Modal Medical Image Synthesis

    • Abstract
    • Acknowledgements
    • 16.1. Introduction
    • 16.2. Supervised Synthesis Using Location-Sensitive Deep Network
    • 16.3. Unsupervised Synthesis Using Mutual Information Maximization
    • 16.4. Conclusions and Future Work
    • References

    17. Natural Language Processing for Large-Scale Medical Image Analysis Using Deep Learning

    • Abstract
    • Acknowledgements
    • 17.1. Introduction
    • 17.2. Fundamentals of Natural Language Processing
    • 17.3. Neural Language Models
    • 17.4. Medical Lexicons
    • 17.5. Predicting Presence or Absence of Frequent Disease Types
    • 17.6. Conclusion
    • References

Product details

  • No. of pages: 458
  • Language: English
  • Copyright: © Academic Press 2017
  • Published: January 18, 2017
  • Imprint: Academic Press
  • Paperback ISBN: 9780128104088
  • eBook ISBN: 9780128104095

About the Editors

S. Kevin Zhou

S. Kevin Zhou
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).

Affiliations and Expertise

Principal Key Expert, Medical Image Analysis, Siemens Healthcare Technology Center, Princeton, New Jersey, USA

Hayit Greenspan

Hayit Greenspan
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.

Affiliations and Expertise

Head, Medical Image Processing and Analysis Lab, Biomedical Engineering Department, Faculty of Engineering, Tel-Aviv University, Israel

Dinggang Shen

Dinggang Shen
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.

Affiliations and Expertise

Professor, Department of Radiology and BRIC, UNC-Chapel Hill, USA

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  • bhushan Fri Jun 07 2019


    nice book