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Machine Learning in Medical Imaging

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Cover of 'Machine Learning in Medical Imaging'

Table of Contents

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    Book Overview
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    Chapter 1 Unsupervised Deep Learning for Hippocampus Segmentation in 7.0 Tesla MR Images
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    Chapter 2 Integrating Multiple Network Properties for MCI Identification
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    Chapter 3 Learning-Boosted Label Fusion for Multi-atlas Auto-Segmentation
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    Chapter 4 Volumetric Segmentation of Key Fetal Brain Structures in 3D Ultrasound
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    Chapter 5 Sparse Classification with MRI Based Markers for Neuromuscular Disease Categorization
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    Chapter 6 Fully Automatic Detection of the Carotid Artery from Volumetric Ultrasound Images Using Anatomical Position-Dependent LBP Features
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    Chapter 7 A Transfer-Learning Approach to Image Segmentation Across Scanners by Maximizing Distribution Similarity
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    Chapter 8 A New Algorithm of Electronic Cleansing for Weak Faecal-Tagging CT Colonography
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    Chapter 9 A Unified Approach to Shape Model Fitting and Non-rigid Registration
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    Chapter 10 A Bayesian Algorithm for Image-Based Time-to-Event Prediction
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    Chapter 11 Machine Learning in Medical Imaging
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    Chapter 12 fMRI Analysis with Sparse Weisfeiler-Lehman Graph Statistics
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    Chapter 13 Patch-Based Segmentation without Registration: Application to Knee MRI
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    Chapter 14 Flow-Based Correspondence Matching in Stereovision
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    Chapter 15 Thickness NETwork (ThickNet) Features for the Detection of Prodromal AD
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    Chapter 16 Metric Space Structures for Computational Anatomy
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    Chapter 17 Discriminative Group Sparse Representation for Mild Cognitive Impairment Classification
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    Chapter 18 Temporally Dynamic Resting-State Functional Connectivity Networks for Early MCI Identification
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    Chapter 19 An Improved Optimization Method for the Relevance Voxel Machine
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    Chapter 20 Disentanglement of Session and Plasticity Effects in Longitudinal fMRI Studies
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    Chapter 21 Identification of Alzheimer’s Disease Using Incomplete Multimodal Dataset via Matrix Shrinkage and Completion
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    Chapter 22 On Feature Relevance in Image-Based Prediction Models: An Empirical Study
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    Chapter 23 Decision Forests with Spatio-Temporal Features for Graph-Based Tumor Segmentation in 4D Lung CT
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    Chapter 24 Improving Probabilistic Image Registration via Reinforcement Learning and Uncertainty Evaluation
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    Chapter 25 HEp-2 Cell Image Classification: A Comparative Analysis
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    Chapter 26 A 2.5D Colon Wall Flattening Model for CT-Based Virtual Colonoscopy
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    Chapter 27 Augmenting Auto-context with Global Geometric Features for Spinal Cord Segmentation
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    Chapter 28 Large-Scale Manifold Learning Using an Adaptive Sparse Neighbor Selection Approach for Brain Tumor Progression Prediction
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    Chapter 29 Ensemble Universum SVM Learning for Multimodal Classification of Alzheimer’s Disease
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    Chapter 30 Joint Sparse Coding Spatial Pyramid Matching for Classification of Color Blood Cell Image
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    Chapter 31 Multi-task Sparse Classifier for Diagnosis of MCI Conversion to AD with Longitudinal MR Images
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    Chapter 32 Sparse Multimodal Manifold-Regularized Transfer Learning for MCI Conversion Prediction
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Title
Machine Learning in Medical Imaging
Published by
Springer International Publishing, September 2013
DOI 10.1007/978-3-319-02267-3
ISBNs
978-3-31-902266-6, 978-3-31-902267-3
Editors

Wu, Guorong, Zhang, Daoqiang, Shen, Dinggang, Yan, Pingkun, Suzuki, Kenji, Wang, Fei

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The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Student > Doctoral Student 1 100%
Readers by discipline Count As %
Engineering 1 100%