Machine Learning in Medical Imaging

Table of Contents

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   Book Overview
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   Chapter 1 Segmentation of Right Ventricle in Cardiac MR Images Using Shape Regression
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   Chapter 2 Visual Saliency Based Active Learning for Prostate MRI Segmentation
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   Chapter 3 Soft-Split Random Forest for Anatomy Labeling
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   Chapter 4 A New Image Data Set and Benchmark for Cervical Dysplasia Classification Evaluation
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   Chapter 5 Machine Learning on High Dimensional Shape Data from Subcortical Brain Surfaces: A Comparison of Feature Selection and Classification Methods
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   Chapter 6 Node-Based Gaussian Graphical Model for Identifying Discriminative Brain Regions from Connectivity Graphs
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   Chapter 7 BundleMAP: Anatomically Localized Features from dMRI for Detection of Disease
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   Chapter 8 FADR: Functional-Anatomical Discriminative Regions for Rest fMRI Characterization
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    Chapter 9 Machine Learning in Medical Imaging
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    Chapter 10 Nonlinear Graph Fusion for Multi-modal Classification of Alzheimer’s Disease
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    Chapter 11 HEp-2 Staining Pattern Recognition Using Stacked Fisher Network for Encoding Weber Local Descriptor
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    Chapter 12 Supervoxel Classification Forests for Estimating Pairwise Image Correspondences
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    Chapter 13 Non-rigid Free-Form 2D-3D Registration Using Statistical Deformation Model
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    Chapter 14 Learning and Combining Image Similarities for Neonatal Brain Population Studies
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    Chapter 15 Deep Learning, Sparse Coding, and SVM for Melanoma Recognition in Dermoscopy Images
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    Chapter 16 Predicting Standard-Dose PET Image from Low-Dose PET and Multimodal MR Images Using Mapping-Based Sparse Representation
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    Chapter 17 Boosting Convolutional Filters with Entropy Sampling for Optic Cup and Disc Image Segmentation from Fundus Images
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    Chapter 18 Brain Fiber Clustering Using Non-negative Kernelized Matching Pursuit
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    Chapter 19 Automatic Detection of Good/Bad Colonies of iPS Cells Using Local Features
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    Chapter 20 Detecting Abnormal Cell Division Patterns in Early Stage Human Embryo Development
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    Chapter 21 Identification of Infants at Risk for Autism Using Multi-parameter Hierarchical White Matter Connectomes.
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    Chapter 22 Group-Constrained Laplacian Eigenmaps: Longitudinal AD Biomarker Learning
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    Chapter 23 Multi-atlas Context Forests for Knee MR Image Segmentation
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    Chapter 24 Longitudinal Patch-Based Segmentation of Multiple Sclerosis White Matter Lesions
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    Chapter 25 Machine Learning in Medical Imaging
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    Chapter 26 Semi-automatic Liver Tumor Segmentation in Dynamic Contrast-Enhanced CT Scans Using Random Forests and Supervoxels
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    Chapter 27 Flexible and Latent Structured Output Learning
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    Chapter 28 Identifying Abnormal Network Alterations Common to Traumatic Brain Injury and Alzheimer’s Disease Patients Using Functional Connectome Data
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    Chapter 29 Multimodal Multi-label Transfer Learning for Early Diagnosis of Alzheimer’s Disease
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    Chapter 30 Soft-Split Sparse Regression Based Random Forest for Predicting Future Clinical Scores of Alzheimer’s Disease
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    Chapter 31 Multi-view Classification for Identification of Alzheimer's Disease
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    Chapter 32 Clustering Analysis for Semi-supervised Learning Improves Classification Performance of Digital Pathology
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    Chapter 33 A Composite of Features for Learning-Based Coronary Artery Segmentation on Cardiac CT Angiography
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    Chapter 34 Ensemble Prostate Tumor Classification in H&E Whole Slide Imaging via Stain Normalization and Cell Density Estimation
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    Chapter 35 Computer-Assisted Diagnosis of Lung Cancer Using Quantitative Topology Features
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    Chapter 36 Machine Learning in Medical Imaging
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    Chapter 37 Nonlinear Feature Transformation and Deep Fusion for Alzheimer’s Disease Staging Analysis
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    Chapter 38 Tumor Classification by Deep Polynomial Network and Multiple Kernel Learning on Small Ultrasound Image Dataset
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    Chapter 39 Multi-source Information Gain for Random Forest: An Application to CT Image Prediction from MRI Data
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    Chapter 40 Joint Learning of Multiple Longitudinal Prediction Models by Exploring Internal Relations