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
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- About the Editors
- Foreword
- Part I: Introduction
-
Part II: Medical Image Detection and Recognition
- Chapter 3: Efficient Medical Image Parsing
- Chapter 4: Multi-Instance Multi-Stage Deep Learning for Medical Image Recognition
- Chapter 5: Automatic Interpretation of Carotid Intima–Media Thickness Videos Using Convolutional Neural Networks
- Chapter 6: Deep Cascaded Networks for Sparsely Distributed Object Detection from Medical Images
- Chapter 7: Deep Voting and Structured Regression for Microscopy Image Analysis
- Part III: Medical Image Segmentation
- Part IV: Medical Image Registration
-
Part V: Computer-Aided Diagnosis and Disease Quantification
-
Chapter 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
- Chapter 14: Deep Learning Models for Classifying Mammogram Exams Containing Unregistered Multi-View Images and Segmentation Maps of Lesions
- Chapter 15: Randomized Deep Learning Methods for Clinical Trial Enrichment and Design in Alzheimer's Disease
-
Chapter 13: Chest Radiograph Pathology Categorization via Transfer Learning
- Part VI: Others
- Index
Product information
- Title: Deep Learning for Medical Image Analysis
- Author(s):
- Release date: January 2017
- Publisher(s): Academic Press
- ISBN: 9780128104095
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