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Deep Learning for Medical Image Analysis

Book 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.

  • 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

Table of Contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. Copyright
  5. Contributors
  6. About the Editors
  7. Foreword
  8. Part I: Introduction
    1. Chapter 1: An Introduction to Neural Networks and Deep Learning
      1. Abstract
      2. 1.1. Introduction
      3. 1.2. Feed-Forward Neural Networks
      4. 1.3. Convolutional Neural Networks
      5. 1.4. Deep Models
      6. 1.5. Tricks for Better Learning
      7. 1.6. Open-Source Tools for Deep Learning
      8. References
    2. Chapter 2: An Introduction to Deep Convolutional Neural Nets for Computer Vision
      1. Abstract
      2. 2.1. Introduction
      3. 2.2. Convolutional Neural Networks
      4. 2.3. CNN Flavors
      5. 2.4. Software for Deep Learning
      6. References
  9. Part II: Medical Image Detection and Recognition
    1. Chapter 3: Efficient Medical Image Parsing
      1. Abstract
      2. 3.1. Introduction
      3. 3.2. Background and Motivation
      4. 3.3. Methodology
      5. 3.4. Experiments
      6. 3.5. Conclusion
      7. Disclaimer
      8. References
    2. Chapter 4: Multi-Instance Multi-Stage Deep Learning for Medical Image Recognition
      1. Abstract
      2. 4.1. Introduction
      3. 4.2. Related Work
      4. 4.3. Methodology
      5. 4.4. Results
      6. 4.5. Discussion and Future Work
      7. References
    3. Chapter 5: Automatic Interpretation of Carotid Intima–Media Thickness Videos Using Convolutional Neural Networks
      1. Abstract
      2. Acknowledgement
      3. 5.1. Introduction
      4. 5.2. Related Work
      5. 5.3. CIMT Protocol
      6. 5.4. Method
      7. 5.5. Experiments
      8. 5.6. Discussion
      9. 5.7. Conclusion
      10. References
    4. Chapter 6: Deep Cascaded Networks for Sparsely Distributed Object Detection from Medical Images
      1. Abstract
      2. Acknowledgements
      3. 6.1. Introduction
      4. 6.2. Method
      5. 6.3. Mitosis Detection from Histology Images
      6. 6.4. Cerebral Microbleed Detection from MR Volumes
      7. 6.5. Discussion and Conclusion
      8. References
    5. Chapter 7: Deep Voting and Structured Regression for Microscopy Image Analysis
      1. Abstract
      2. Acknowledgements
      3. 7.1. Deep Voting: A Robust Approach Toward Nucleus Localization in Microscopy Images
      4. 7.2. Structured Regression for Robust Cell Detection Using Convolutional Neural Network
      5. References
  10. Part III: Medical Image Segmentation
    1. Chapter 8: Deep Learning Tissue Segmentation in Cardiac Histopathology Images
      1. Abstract
      2. 8.1. Introduction
      3. 8.2. Experimental Design and Implementation
      4. 8.3. Results and Discussion
      5. 8.4. Concluding Remarks
      6. Notes
      7. Disclosure Statement
      8. Funding
      9. References
    2. Chapter 9: Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching
      1. Abstract
      2. 9.1. Background
      3. 9.2. Proposed Method
      4. 9.3. Experiments
      5. 9.4. Conclusion
      6. References
    3. Chapter 10: Characterization of Errors in Deep Learning-Based Brain MRI Segmentation
      1. Abstract
      2. 10.1. Introduction
      3. 10.2. Deep Learning for Segmentation
      4. 10.3. Convolutional Neural Network Architecture
      5. 10.4. Experiments
      6. 10.5. Results
      7. 10.6. Discussion
      8. 10.7. Conclusion
      9. References
  11. Part IV: Medical Image Registration
    1. Chapter 11: Scalable High Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning
      1. Abstract
      2. 11.1. Introduction
      3. 11.2. Proposed Method
      4. 11.3. Experiments
      5. 11.4. Conclusion
      6. References
    2. Chapter 12: Convolutional Neural Networks for Robust and Real-Time 2-D/3-D Registration
      1. Abstract
      2. 12.1. Introduction
      3. 12.2. X-Ray Imaging Model
      4. 12.3. Problem Formulation
      5. 12.4. Regression Strategy
      6. 12.5. Feature Extraction
      7. 12.6. Convolutional Neural Network
      8. 12.7. Experiments and Results
      9. 12.8. Discussion
      10. Disclaimer
      11. References
  12. Part V: Computer-Aided Diagnosis and Disease Quantification
    1. Chapter 13: Chest Radiograph Pathology Categorization via Transfer Learning
      1. Abstract
      2. Acknowledgements
      3. 13.1. Introduction
      4. 13.2. Image Representation Schemes with Classical (Non-Deep) Features
      5. 13.3. Extracting Deep Features from a Pre-Trained CNN Model
      6. 13.4. Extending the Representation Using Feature Fusion and Selection
      7. 13.5. Experiments and Results
      8. 13.6. Conclusion
      9. References
    2. Chapter 14: Deep Learning Models for Classifying Mammogram Exams Containing Unregistered Multi-View Images and Segmentation Maps of Lesions
      1. Abstract
      2. Acknowledgements
      3. 14.1. Introduction
      4. 14.2. Literature Review
      5. 14.3. Methodology
      6. 14.4. Materials and Methods
      7. 14.5. Results
      8. 14.6. Discussion
      9. 14.7. Conclusion
      10. References
    3. Chapter 15: Randomized Deep Learning Methods for Clinical Trial Enrichment and Design in Alzheimer's Disease
      1. Abstract
      2. Acknowledgements
      3. 15.1. Introduction
      4. 15.2. Background
      5. 15.3. Optimal Enrichment Criterion
      6. 15.4. Randomized Deep Networks
      7. 15.5. Experiments
      8. 15.6. Discussion
      9. References
  13. Part VI: Others
    1. Chapter 16: Deep Networks and Mutual Information Maximization for Cross-Modal Medical Image Synthesis
      1. Abstract
      2. Acknowledgements
      3. 16.1. Introduction
      4. 16.2. Supervised Synthesis Using Location-Sensitive Deep Network
      5. 16.3. Unsupervised Synthesis Using Mutual Information Maximization
      6. 16.4. Conclusions and Future Work
      7. References
    2. Chapter 17: Natural Language Processing for Large-Scale Medical Image Analysis Using Deep Learning
      1. Abstract
      2. Acknowledgements
      3. 17.1. Introduction
      4. 17.2. Fundamentals of Natural Language Processing
      5. 17.3. Neural Language Models
      6. 17.4. Medical Lexicons
      7. 17.5. Predicting Presence or Absence of Frequent Disease Types
      8. 17.6. Conclusion
      9. References
  14. Index