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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
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Machine Learning in Medical Imaging
Published by
Springer International Publishing, January 2014
DOI 10.1007/978-3-319-10581-9
978-3-31-910580-2, 978-3-31-910581-9

Guorong Wu, Daoqiang Zhang, Luping Zhou

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Mendeley readers

The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 10 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 10%
Student > Bachelor 1 10%
Researcher 1 10%
Unknown 7 70%
Readers by discipline Count As %
Computer Science 1 10%
Physics and Astronomy 1 10%
Neuroscience 1 10%
Unknown 7 70%