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Radiomics and Radiogenomics in Neuro-oncology

Overview of attention for book
Cover of 'Radiomics and Radiogenomics in Neuro-oncology'

Table of Contents

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    Book Overview
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    Chapter 1 Current Status of the Use of Machine Learning and Magnetic Resonance Imaging in the Field of Neuro-Radiomics
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    Chapter 2 Opportunities and Advances in Radiomics and Radiogenomics in Neuro-Oncology
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    Chapter 3 A Survey on Recent Advancements for AI Enabled Radiomics in Neuro-Oncology
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    Chapter 4 Deep Radiomic Features from MRI Scans Predict Survival Outcome of Recurrent Glioblastoma
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    Chapter 5 cuRadiomics: A GPU-Based Radiomics Feature Extraction Toolkit
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    Chapter 6 On Validating Multimodal MRI Based Stratification of IDH Genotype in High Grade Gliomas Using CNNs and Its Comparison to Radiomics
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    Chapter 7 Imaging Signature of 1p/19q Co-deletion Status Derived via Machine Learning in Lower Grade Glioma
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    Chapter 8 A Feature-Pooling and Signature-Pooling Method for Feature Selection for Quantitative Image Analysis: Application to a Radiomics Model for Survival in Glioma
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    Chapter 9 Radiomics-Enhanced Multi-task Neural Network for Non-invasive Glioma Subtyping and Segmentation
Attention for Chapter 7: Imaging Signature of 1p/19q Co-deletion Status Derived via Machine Learning in Lower Grade Glioma
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Chapter title
Imaging Signature of 1p/19q Co-deletion Status Derived via Machine Learning in Lower Grade Glioma
Chapter number 7
Book title
Radiomics and Radiogenomics in Neuro-oncology
Published by
Springer, Cham, February 2020
DOI 10.1007/978-3-030-40124-5_7
Book ISBNs
978-3-03-040123-8, 978-3-03-040124-5
Authors

Saima Rathore, Ahmad Chaddad, Nadeem Haider Bukhari, Tamim Niazi, Rathore, Saima, Chaddad, Ahmad, Bukhari, Nadeem Haider, Niazi, Tamim

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Student > Doctoral Student 1 33%
Student > Master 1 33%
Unknown 1 33%
Readers by discipline Count As %
Medicine and Dentistry 1 33%
Engineering 1 33%
Unknown 1 33%

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