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Patch-Based Techniques in Medical Imaging

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Cover of 'Patch-Based Techniques in Medical Imaging'

Table of Contents

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    Book Overview
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    Chapter 1 4D Multi-atlas Label Fusion Using Longitudinal Images
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    Chapter 2 Brain Image Labeling Using Multi-atlas Guided 3D Fully Convolutional Networks
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    Chapter 3 Whole Brain Parcellation with Pathology: Validation on Ventriculomegaly Patients
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    Chapter 4 Hippocampus Subfield Segmentation Using a Patch-Based Boosted Ensemble of Autocontext Neural Networks
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    Chapter 5 On the Role of Patch Spaces in Patch-Based Label Fusion
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    Chapter 6 Learning a Sparse Database for Patch-Based Medical Image Segmentation
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    Chapter 7 Accurate and High Throughput Cell Segmentation Method for Mouse Brain Nuclei Using Cascaded Convolutional Neural Network
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    Chapter 8 Learning-Based Estimation of Functional Correlation Tensors in White Matter for Early Diagnosis of Mild Cognitive Impairment
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    Chapter 9 Early Prediction of Alzheimer’s Disease with Non-local Patch-Based Longitudinal Descriptors
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    Chapter 10 Adaptive Fusion of Texture-Based Grading: Application to Alzheimer’s Disease Detection
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    Chapter 11 Micro-CT Guided 3D Reconstruction of Histological Images
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    Chapter 12 A Neural Regression Framework for Low-Dose Coronary CT Angiography (CCTA) Denoising
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    Chapter 13 A Dictionary Learning-Based Fast Imaging Method for Ultrasound Elastography
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    Chapter 14 Breast Tumor Detection in Ultrasound Images Using Deep Learning
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    Chapter 15 Modeling the Intra-class Variability for Liver Lesion Detection Using a Multi-class Patch-Based CNN
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    Chapter 16 Multiple Sclerosis Lesion Segmentation Using Joint Label Fusion
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    Chapter 17 Deep Multimodal Case–Based Retrieval for Large Histopathology Datasets
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    Chapter 18 Sparse Representation Using Block Decomposition for Characterization of Imaging Patterns
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Title
Patch-Based Techniques in Medical Imaging
Published by
Springer, Cham, January 2017
DOI 10.1007/978-3-319-67434-6
ISBNs
978-3-31-967433-9, 978-3-31-967434-6
Editors

Guorong Wu, Brent C. Munsell, Yiqiang Zhan, Wenjia Bai, Gerard Sanroma, Pierrick Coupé

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The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 68 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 1%
Turkey 1 1%
Canada 1 1%
Unknown 65 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 25 37%
Student > Master 12 18%
Student > Doctoral Student 7 10%
Student > Bachelor 5 7%
Professor 2 3%
Other 9 13%
Unknown 8 12%
Readers by discipline Count As %
Computer Science 29 43%
Engineering 14 21%
Medicine and Dentistry 5 7%
Agricultural and Biological Sciences 3 4%
Physics and Astronomy 1 1%
Other 2 3%
Unknown 14 21%