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Radiomics and Radiogenomics in Neuro-oncology: First International Workshop, RNO-AI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings de Hassan Mohy-ud-Din

Radiomics and Radiogenomics in Neuro-oncology: First International Workshop, RNO-AI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings: Lecture Notes in Computer Science, cartea 11991

Editat de Hassan Mohy-ud-Din, Saima Rathore
en Limba Engleză Paperback – 25 feb 2020
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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Specificații

ISBN-13: 9783030401238
ISBN-10: 3030401235
Pagini: 91
Ilustrații: IX, 91 p. 22 illus., 19 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.16 kg
Ediția:1st ed. 2020
Editura: Springer International Publishing
Colecția Springer
Seriile Lecture Notes in Computer Science, Image Processing, Computer Vision, Pattern Recognition, and Graphics

Locul publicării:Cham, Switzerland

Cuprins

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.