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Graphs in Biomedical Image Analysis and Integrating Medical Imaging and Non-Imaging Modalities

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Cover of 'Graphs in Biomedical Image Analysis and Integrating Medical Imaging and Non-Imaging Modalities'

Table of Contents

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    Book Overview
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    Chapter 1 Graph Saliency Maps Through Spectral Convolutional Networks: Application to Sex Classification with Brain Connectivity
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    Chapter 2 A Graph Representation and Similarity Measure for Brain Networks with Nodal Features
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    Chapter 3 Multi-modal Disease Classification in Incomplete Datasets Using Geometric Matrix Completion
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    Chapter 4 BrainParcel: A Brain Parcellation Algorithm for Cognitive State Classification
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    Chapter 5 Modeling Brain Networks with Artificial Neural Networks
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    Chapter 6 A Bayesian Disease Progression Model for Clinical Trajectories
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    Chapter 7 Multi-modal Brain Connectivity Study Using Deep Collaborative Learning
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    Chapter 8 Towards Subject and Diagnostic Identifiability in the Alzheimer’s Disease Spectrum Based on Functional Connectomes
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    Chapter 9 Predicting Conversion of Mild Cognitive Impairments to Alzheimer’s Disease and Exploring Impact of Neuroimaging
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    Chapter 10 Cross-diagnostic Prediction of Dimensional Psychiatric Phenotypes in Anorexia Nervosa and Body Dysmorphic Disorder Using Multimodal Neuroimaging and Psychometric Data
Attention for Chapter 1: Graph Saliency Maps Through Spectral Convolutional Networks: Application to Sex Classification with Brain Connectivity
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Chapter title
Graph Saliency Maps Through Spectral Convolutional Networks: Application to Sex Classification with Brain Connectivity
Chapter number 1
Book title
Graphs in Biomedical Image Analysis and Integrating Medical Imaging and Non-Imaging Modalities
Published by
Springer, Cham, September 2018
DOI 10.1007/978-3-030-00689-1_1
Book ISBNs
978-3-03-000688-4, 978-3-03-000689-1
Authors

Salim Arslan, Sofia Ira Ktena, Ben Glocker, Daniel Rueckert, Arslan, Salim, Ktena, Sofia Ira, Glocker, Ben, Rueckert, Daniel

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X Demographics

The data shown below were collected from the profiles of 7 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 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 65 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 23%
Student > Master 12 18%
Student > Bachelor 5 8%
Researcher 4 6%
Student > Doctoral Student 3 5%
Other 10 15%
Unknown 16 25%
Readers by discipline Count As %
Computer Science 26 40%
Engineering 5 8%
Agricultural and Biological Sciences 4 6%
Neuroscience 4 6%
Medicine and Dentistry 2 3%
Other 5 8%
Unknown 19 29%

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