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Radiomics and Radiogenomics in Neuro-oncology [electronic resource] : First International Workshop, RNO-AI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings / edited by Hassan Mohy-ud-Din, Saima Rathore.

Contributor(s): Mohy-ud-Din, Hassan [editor.] | Rathore, Saima [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Image Processing, Computer Vision, Pattern Recognition, and Graphics: 11991Publisher: Cham : Springer International Publishing : Imprint: Springer, 2020Edition: 1st ed. 2020.Description: IX, 91 p. 22 illus., 19 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783030401245.Subject(s): Computer vision | Machine learning | Computer networks  | Application software | Pattern recognition systems | Computer Vision | Machine Learning | Computer Communication Networks | Computer and Information Systems Applications | Automated Pattern RecognitionAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 006.37 Online resources: Click here to access online
Contents:
Current Status of the Use of Machine Learning and Magnetic Resonance Imaging in the Field of Neuro- Radiomics -- Opportunities and Advances in Radiomics and Radiogenomics in Neuro-Oncology -- A Survey on Recent Advancements for AI Enabled Radiomics in Neuro-Oncology -- Multimodal MRI for Radiogenomic Analysis of PTEN Mutation in Glioblastoma -- Deep radiomic features from MRI scans predict survival outcome of recurrent glio-blastoma -- cuRadiomics: A GPU-based Radiomics Feature Extraction Toolkit -- On validating multimodal MRI based stratification of IDH genotype in high grade gliomas using CNNs and its comparison to radiomics -- Imaging signature of 1p/19q co-deletion status derived via machine learning in lower grade glioma -- A feature-pooling and signature-pooling method for feature selection for quantitative image analysis: application to a radiomics model for survival in glioma -- Radiomics-Enhanced Multi-Task Neural Network for Non-invasive Glioma Subtyp-ing and Segmentation. .
In: Springer Nature eBookSummary: This book constitutes the proceedings of the First International Workshop on Radiomics and Radiogenomics in Neuro-oncology, RNO-AI 2019, which was held in conjunction with MICCAI in Shenzhen, China, in October 2019. The 10 full papers presented in this volume were carefully reviewed and selected from 15 submissions. They deal with the development of tools that can automate the analysis and synthesis of neuro-oncologic imaging. .
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Current Status of the Use of Machine Learning and Magnetic Resonance Imaging in the Field of Neuro- Radiomics -- Opportunities and Advances in Radiomics and Radiogenomics in Neuro-Oncology -- A Survey on Recent Advancements for AI Enabled Radiomics in Neuro-Oncology -- Multimodal MRI for Radiogenomic Analysis of PTEN Mutation in Glioblastoma -- Deep radiomic features from MRI scans predict survival outcome of recurrent glio-blastoma -- cuRadiomics: A GPU-based Radiomics Feature Extraction Toolkit -- On validating multimodal MRI based stratification of IDH genotype in high grade gliomas using CNNs and its comparison to radiomics -- Imaging signature of 1p/19q co-deletion status derived via machine learning in lower grade glioma -- A feature-pooling and signature-pooling method for feature selection for quantitative image analysis: application to a radiomics model for survival in glioma -- Radiomics-Enhanced Multi-Task Neural Network for Non-invasive Glioma Subtyp-ing and Segmentation. .

This book constitutes the proceedings of the First International Workshop on Radiomics and Radiogenomics in Neuro-oncology, RNO-AI 2019, which was held in conjunction with MICCAI in Shenzhen, China, in October 2019. The 10 full papers presented in this volume were carefully reviewed and selected from 15 submissions. They deal with the development of tools that can automate the analysis and synthesis of neuro-oncologic imaging. .

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