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Simulation and Synthesis in Medical Imaging

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Cover of 'Simulation and Synthesis in Medical Imaging'

Table of Contents

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    Book Overview
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    Chapter 1 Empirical Bayesian Mixture Models for Medical Image Translation
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    Chapter 2 Improved MR to CT Synthesis for PET/MR Attenuation Correction Using Imitation Learning
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    Chapter 3 Unpaired Multi-contrast MR Image Synthesis Using Generative Adversarial Networks
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    Chapter 4 Unsupervised Retina Image Synthesis via Disentangled Representation Learning
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    Chapter 5 Pseudo-normal PET Synthesis with Generative Adversarial Networks for Localising Hypometabolism in Epilepsies
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    Chapter 6 Breast Mass Detection in Mammograms via Blending Adversarial Learning
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    Chapter 7 Tunable CT Lung Nodule Synthesis Conditioned on Background Image and Semantic Features
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    Chapter 8 Mask2Lesion: Mask-Constrained Adversarial Skin Lesion Image Synthesis
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    Chapter 9 Towards Annotation-Free Segmentation of Fluorescently Labeled Cell Membranes in Confocal Microscopy Images
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    Chapter 10 Intelligent Image Synthesis to Attack a Segmentation CNN Using Adversarial Learning
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    Chapter 11 Physics-Informed Brain MRI Segmentation
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    Chapter 12 3D Medical Image Synthesis by Factorised Representation and Deformable Model Learning
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    Chapter 13 Cycle-Consistent Training for Reducing Negative Jacobian Determinant in Deep Registration Networks
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    Chapter 14 iSMORE: An Iterative Self Super-Resolution Algorithm
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    Chapter 15 An Optical Model of Whole Blood for Detecting Platelets in Lens-Free Images
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    Chapter 16 Evaluation of the Realism of an MRI Simulator for Stroke Lesion Prediction Using Convolutional Neural Network
Attention for Chapter 1: Empirical Bayesian Mixture Models for Medical Image Translation
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Chapter title
Empirical Bayesian Mixture Models for Medical Image Translation
Chapter number 1
Book title
Simulation and Synthesis in Medical Imaging
Published by
Springer, Cham, October 2019
DOI 10.1007/978-3-030-32778-1_1
Book ISBNs
978-3-03-032777-4, 978-3-03-032778-1
Authors

Mikael Brudfors, John Ashburner, Parashkev Nachev, Yaël Balbastre, Brudfors, Mikael, Ashburner, John, Nachev, Parashkev, Balbastre, Yaël

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 31%
Researcher 3 23%
Professor 2 15%
Student > Master 1 8%
Unknown 3 23%
Readers by discipline Count As %
Engineering 3 23%
Physics and Astronomy 1 8%
Computer Science 1 8%
Medicine and Dentistry 1 8%
Social Sciences 1 8%
Other 0 0%
Unknown 6 46%