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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 Sparsity-Learning-Based Longitudinal MR Image Registration for Early Brain Development
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    Chapter 2 Graph-Based Label Propagation in Fetal Brain MR Images
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    Chapter 3 Deep Learning Based Automatic Immune Cell Detection for Immunohistochemistry Images
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    Chapter 4 Stacked Multiscale Feature Learning for Domain Independent Medical Image Segmentation
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    Chapter 5 Detection of Mammographic Masses by Content-Based Image Retrieval
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    Chapter 6 Inferring Sources of Dementia Progression with Network Diffusion Model
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    Chapter 7 3D Intervertebral Disc Localization and Segmentation from MR Images by Data-Driven Regression and Classification
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    Chapter 8 Exploring Compact Representation of SICE Matrices for Functional Brain Network Classification
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    Chapter 9 Deep Learning for Cerebellar Ataxia Classification and Functional Score Regression.
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    Chapter 10 Manifold Alignment and Transfer Learning for Classification of Alzheimer’s Disease
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    Chapter 11 Gleason Grading of Prostate Tumours with Max-Margin Conditional Random Fields
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    Chapter 12 Learning Distance Transform for Boundary Detection and Deformable Segmentation in CT Prostate Images
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    Chapter 13 Geodesic Geometric Mean of Regional Covariance Descriptors as an Image-Level Descriptor for Nuclear Atypia Grading in Breast Histology Images
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    Chapter 14 A Constrained Regression Forests Solution to 3D Fetal Ultrasound Plane Localization for Longitudinal Analysis of Brain Growth and Maturation
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    Chapter 15 Deep Learning of Image Features from Unlabeled Data for Multiple Sclerosis Lesion Segmentation
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    Chapter 16 Fetal Abdominal Standard Plane Localization through Representation Learning with Knowledge Transfer
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    Chapter 17 Searching for Structures of Interest in an Ultrasound Video Sequence
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    Chapter 18 Anatomically Constrained Weak Classifier Fusion for Early Detection of Alzheimer’s Disease
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    Chapter 19 Automatic Bone and Marrow Extraction from Dual Energy CT through SVM Margin-Based Multi-Material Decomposition Model Selection
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    Chapter 20 Sparse Discriminative Feature Selection for Multi-class Alzheimer’s Disease Classification
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    Chapter 21 Context-Aware Anatomical Landmark Detection: Application to Deformable Model Initialization in Prostate CT Images
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    Chapter 22 Optimal MAP Parameters Estimation in STAPLE - Learning from Performance Parameters versus Image Similarity Information
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    Chapter 23 Colon Biopsy Classification Using Crypt Architecture
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    Chapter 24 Network-Guided Group Feature Selection for Classification of Autism Spectrum Disorder
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    Chapter 25 Deformation field correction for spatial normalization of PET images using a population-derived partial least squares model.
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    Chapter 26 Novel Multi-Atlas Segmentation by Matrix Completion
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    Chapter 27 Structured Random Forests for Myocardium Delineation in 3D Echocardiography
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    Chapter 28 Improved Reproducibility of Neuroanatomical Definitions through Diffeomorphometry and Complexity Reduction
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    Chapter 29 Topological Descriptors of Histology Images
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    Chapter 30 Robust Deep Learning for Improved Classification of AD/MCI Patients
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    Chapter 31 Subject Specific Sparse Dictionary Learning for Atlas based Brain MRI Segmentation.
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    Chapter 32 Multi-atlas Segmentation with Learning-Based Label Fusion
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    Chapter 33 Interactive Prostate Segmentation Based on Adaptive Feature Selection and Manifold Regularization
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    Chapter 34 Feature Selection Based on SVM Significance Maps for Classification of Dementia
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    Chapter 35 Prediction of Standard-Dose PET Image by Low-Dose PET and MRI Images
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    Chapter 36 Solutions for Missing Parameters in Computer-Aided Diagnosis with Multiparametric Imaging Data
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    Chapter 37 Online Discriminative Multi-atlas Learning for Isointense Infant Brain Segmentation
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    Chapter 38 Persistent Reeb Graph Matching for Fast Brain Search.
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    Chapter 39 In Vivo MRI Based Prostate Cancer Identification with Random Forests and Auto-context Model
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    Chapter 40 Learning of Atlas Forest Hierarchy for Automatic Labeling of MR Brain Images
Attention for Chapter 8: Exploring Compact Representation of SICE Matrices for Functional Brain Network Classification
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Chapter title
Exploring Compact Representation of SICE Matrices for Functional Brain Network Classification
Chapter number 8
Book title
Machine Learning in Medical Imaging
Published by
Springer International Publishing, September 2014
DOI 10.1007/978-3-319-10581-9_8
Book ISBNs
978-3-31-910580-2, 978-3-31-910581-9
Authors

Jianjia Zhang, Luping Zhou, Lei Wang, Wanqing Li

Editors

Guorong Wu, Daoqiang Zhang, Luping Zhou

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Professor 1 17%
Student > Ph. D. Student 1 17%
Professor > Associate Professor 1 17%
Unknown 3 50%
Readers by discipline Count As %
Computer Science 2 33%
Unknown 4 67%