About the Book:
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.
Table of Contents
Chapter 1: An Introduction to Neural Networks and Deep LearningChapter 2: An Introduction to Deep Convolutional Neural Nets for Computer VisionChapter 3: Efficient Medical Image ParsingChapter 4: Multi-Instance Multi-Stage Deep Learning for Medical Image RecognitionChapter 5: Automatic Interpretation of Carotid Intima–Media Thickness Videos Using Convolutional Neural NetworksChapter 6: Deep Cascaded Networks for Sparsely Distributed Object Detection from Medical ImagesChapter 7: Deep Voting and Structured Regression for Microscopy Image AnalysisChapter 8: Deep Learning Tissue Segmentation in Cardiac Histopathology ImagesChapter 9: Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch MatchingChapter 10: Characterization of Errors in Deep Learning-Based Brain MRI SegmentationChapter 11: Scalable High Performance Image Registration Framework by Unsupervised Deep Feature Representations LearningChapter 12: Convolutional Neural Networks for Robust and Real-Time 2-D/3-D RegistrationChapter 13: Chest Radiograph Pathology Categorization via Transfer LearningChapter 14: Deep Learning Models for Classifying Mammogram Exams Containing Unregistered Multi-View Images and Segmentation Maps of LesionsChapter 15: Randomized Deep Learning Methods for Clinical Trial Enrichment and Design in Alzheimer-s DiseaseChapter 16: Deep Networks and Mutual Information Maximization for Cross-Modal Medical Image SynthesisChapter 17: Natural Language Processing for Large-Scale Medical Image Analysis Using Deep Learning
