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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 Learning Real Noise for Ultra-Low Dose Lung CT Denoising
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    Chapter 2 MRI Denoising Using Deep Learning
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    Chapter 3 MRI Denoising and Artefact Removal Using Self-Organizing Maps for Fast Global Block-Matching
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    Chapter 4 A Monte Carlo Framework for Denoising and Missing Wedge Reconstruction in Cryo-electron Tomography
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    Chapter 5 Robust Supervoxel Matching Combining Mid-Level Spectral and Context-Rich Features
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    Chapter 6 Stereo Matching for Wireless Capsule Endoscopy Using Direct Attenuation Model
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    Chapter 7 Liver Tissue Classification Using an Auto-context-based Deep Neural Network with a Multi-phase Training Framework
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    Chapter 8 Using 1D Patch-Based Signatures for Efficient Cascaded Classification of Lung Nodules
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    Chapter 9 Predicting Future Bone Infiltration Patterns in Multiple Myeloma
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    Chapter 10 A Fast Automatic Juxta-pleural Lung Nodule Detection Framework Using Convolutional Neural Networks and Vote Algorithm
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    Chapter 11 LesionBrain: An Online Tool for White Matter Lesion Segmentation
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    Chapter 12 Multi-atlas Parcellation in the Presence of Lesions: Application to Multiple Sclerosis
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    Chapter 13 A Patch-Based Segmentation Approach with High Level Representation of the Data for Cortical Sulci Recognition
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    Chapter 14 Tumor Delineation for Brain Radiosurgery by a ConvNet and Non-uniform Patch Generation
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    Chapter 15 Iterative Deep Retinal Topology Extraction
Attention for Chapter 7: Liver Tissue Classification Using an Auto-context-based Deep Neural Network with a Multi-phase Training Framework
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Chapter title
Liver Tissue Classification Using an Auto-context-based Deep Neural Network with a Multi-phase Training Framework
Chapter number 7
Book title
Patch-Based Techniques in Medical Imaging
Published by
Springer, Cham, September 2018
DOI 10.1007/978-3-030-00500-9_7
Pubmed ID
Book ISBNs
978-3-03-000499-6, 978-3-03-000500-9
Authors

Fan Zhang, Junlin Yang, Nariman Nezami, Fabian Laage-gaupp, Julius Chapiro, Ming De Lin, James Duncan, Zhang, Fan, Yang, Junlin, Nezami, Nariman, Laage-gaupp, Fabian, Chapiro, Julius, Lin, Ming De, Duncan, James, De Lin, Ming

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 29 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 21%
Other 3 10%
Student > Bachelor 3 10%
Researcher 3 10%
Student > Master 3 10%
Other 2 7%
Unknown 9 31%
Readers by discipline Count As %
Computer Science 6 21%
Engineering 4 14%
Medicine and Dentistry 2 7%
Economics, Econometrics and Finance 1 3%
Unspecified 1 3%
Other 2 7%
Unknown 13 45%