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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 6: Stereo Matching for Wireless Capsule Endoscopy Using Direct Attenuation Model
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Chapter title
Stereo Matching for Wireless Capsule Endoscopy Using Direct Attenuation Model
Chapter number 6
Book title
Patch-Based Techniques in Medical Imaging
Published by
Springer, Cham, September 2018
DOI 10.1007/978-3-030-00500-9_6
Book ISBNs
978-3-03-000499-6, 978-3-03-000500-9
Authors

Min-Gyu Park, Ju Hong Yoon, Youngbae Hwang, Park, Min-Gyu, Yoon, Ju Hong, Hwang, Youngbae

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Professor > Associate Professor 1 20%
Other 1 20%
Unknown 3 60%
Readers by discipline Count As %
Computer Science 1 20%
Medicine and Dentistry 1 20%
Unknown 3 60%