book

Deep Learning for Medical Image Analysis

Name: Deep Learning for Medical Image Analysis
ISBN: 9780128104095

by S. Kevin Zhou, Hayit Greenspan, Dinggang Shen

January 2017

Intermediate to advanced

458 pages

22h 49m

English

Academic Press

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Cover image
Title page
Table of Contents
Copyright
Contributors
About the Editors
Foreword
Part I: Introduction
Chapter 1: An Introduction to Neural Networks and Deep Learning
Abstract1.1. Introduction1.2. Feed-Forward Neural Networks1.3. Convolutional Neural Networks1.4. Deep Models1.5. Tricks for Better Learning1.6. Open-Source Tools for Deep LearningReferences
Chapter 2: An Introduction to Deep Convolutional Neural Nets for Computer Vision
Abstract2.1. Introduction2.2. Convolutional Neural Networks2.3. CNN Flavors2.4. Software for Deep LearningReferences

Part II: Medical Image Detection and Recognition
Chapter 3: Efficient Medical Image Parsing
Abstract3.1. Introduction3.2. Background and Motivation3.3. Methodology3.4. Experiments3.5. ConclusionDisclaimerReferences
Chapter 4: Multi-Instance Multi-Stage Deep Learning for Medical Image Recognition
Abstract4.1. Introduction4.2. Related Work4.3. Methodology4.4. Results4.5. Discussion and Future WorkReferences
Chapter 5: Automatic Interpretation of Carotid Intima–Media Thickness Videos Using Convolutional Neural Networks
AbstractAcknowledgement5.1. Introduction5.2. Related Work5.3. CIMT Protocol5.4. Method5.5. Experiments5.6. Discussion5.7. ConclusionReferences
Chapter 6: Deep Cascaded Networks for Sparsely Distributed Object Detection from Medical Images
AbstractAcknowledgements6.1. Introduction6.2. Method6.3. Mitosis Detection from Histology Images6.4. Cerebral Microbleed Detection from MR Volumes6.5. Discussion and ConclusionReferences
Chapter 7: Deep Voting and Structured Regression for Microscopy Image Analysis
AbstractAcknowledgements7.1. Deep Voting: A Robust Approach Toward Nucleus Localization in Microscopy Images7.2. Structured Regression for Robust Cell Detection Using Convolutional Neural NetworkReferences
Part III: Medical Image Segmentation
Chapter 8: Deep Learning Tissue Segmentation in Cardiac Histopathology Images
Abstract8.1. Introduction8.2. Experimental Design and Implementation8.3. Results and Discussion8.4. Concluding RemarksNotesDisclosure StatementFundingReferences
Chapter 9: Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching
Abstract9.1. Background9.2. Proposed Method9.3. Experiments9.4. ConclusionReferences
Chapter 10: Characterization of Errors in Deep Learning-Based Brain MRI Segmentation
Abstract10.1. Introduction10.2. Deep Learning for Segmentation10.3. Convolutional Neural Network Architecture10.4. Experiments10.5. Results10.6. Discussion10.7. ConclusionReferences
Part IV: Medical Image Registration
Chapter 11: Scalable High Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning
Abstract11.1. Introduction11.2. Proposed Method11.3. Experiments11.4. ConclusionReferences
Chapter 12: Convolutional Neural Networks for Robust and Real-Time 2-D/3-D Registration
Abstract12.1. Introduction12.2. X-Ray Imaging Model12.3. Problem Formulation12.4. Regression Strategy12.5. Feature Extraction12.6. Convolutional Neural Network12.7. Experiments and Results12.8. DiscussionDisclaimerReferences
Part V: Computer-Aided Diagnosis and Disease Quantification
Chapter 13: Chest Radiograph Pathology Categorization via Transfer Learning
AbstractAcknowledgements13.1. Introduction13.2. Image Representation Schemes with Classical (Non-Deep) Features13.3. Extracting Deep Features from a Pre-Trained CNN Model13.4. Extending the Representation Using Feature Fusion and Selection13.5. Experiments and Results13.6. ConclusionReferences
Chapter 14: Deep Learning Models for Classifying Mammogram Exams Containing Unregistered Multi-View Images and Segmentation Maps of Lesions
AbstractAcknowledgements14.1. Introduction14.2. Literature Review14.3. Methodology14.4. Materials and Methods14.5. Results14.6. Discussion14.7. ConclusionReferences
Chapter 15: Randomized Deep Learning Methods for Clinical Trial Enrichment and Design in Alzheimer's Disease
AbstractAcknowledgements15.1. Introduction15.2. Background15.3. Optimal Enrichment Criterion15.4. Randomized Deep Networks15.5. Experiments15.6. DiscussionReferences
Part VI: Others
Chapter 16: Deep Networks and Mutual Information Maximization for Cross-Modal Medical Image Synthesis
AbstractAcknowledgements16.1. Introduction16.2. Supervised Synthesis Using Location-Sensitive Deep Network16.3. Unsupervised Synthesis Using Mutual Information Maximization16.4. Conclusions and Future WorkReferences
Chapter 17: Natural Language Processing for Large-Scale Medical Image Analysis Using Deep Learning
AbstractAcknowledgements17.1. Introduction17.2. Fundamentals of Natural Language Processing17.3. Neural Language Models17.4. Medical Lexicons17.5. Predicting Presence or Absence of Frequent Disease Types17.6. ConclusionReferences
Index

Content preview from Deep Learning for Medical Image Analysis

Chapter 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

Chapter 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

Chapter 3: Efficient Medical Image Parsing

Abstract

3.1. Introduction ...

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Deep Learning for Medical Image Analysis

by S. Kevin Zhou, Hayit Greenspan, Dinggang Shen

Table of Contents

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.