Radiomics and Radiogenomics in Neuro-oncology
First International Workshop, RNO-AI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings
Hassan Mohy-ud-Din (Redaktør) ; Saima Rathore (Redaktør)
Serie: Image Processing, Computer Vision, Pattern Recognition, and Graphics 11991
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. Les mer
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Vår pris:
759,-
(Paperback)
Fri frakt!
Leveringstid: Sendes innen 21 dager
På grunn av Brexit-tilpasninger og tiltak for å begrense covid-19 kan det dessverre oppstå forsinket levering.
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.
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.
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.