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ORCIDRNO-AI@MICCAI 2019, Shenzhen, China
- Hassan Mohy-ud-Din
, Saima Rathore:
Radiomics and Radiogenomics in Neuro-oncology - First International Workshop, RNO-AI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, Proceedings. Lecture Notes in Computer Science 11991, Springer 2020, ISBN 978-3-030-40123-8 - Ashish Singh, Michel Bilello:
Current Status of the Use of Machine Learning and Magnetic Resonance Imaging in the Field of Neuro-Radiomics. 1-11 - Kaustav Bera, Niha G. Beig, Pallavi Tiwari:
Opportunities and Advances in Radiomics and Radiogenomics in Neuro-Oncology. 12-23 - Syed Muhammad Anwar, Tooba Altaf, Khola Rafique, Harish RaviPrakash, Hassan Mohy-ud-Din, Ulas Bagci:
A Survey on Recent Advancements for AI Enabled Radiomics in Neuro-Oncology. 24-35 - Ahmad Chaddad, Mingli Zhang, Christian Desrosiers, Tamim Niazi:
Deep Radiomic Features from MRI Scans Predict Survival Outcome of Recurrent Glioblastoma. 36-43 - Yining Jiao, Oihane Mayo Ijurra, Lichi Zhang, Dinggang Shen, Qian Wang:
cuRadiomics: A GPU-Based Radiomics Feature Extraction Toolkit. 44-52 - Tanay Chougule, Sumeet Shinde, Vani Santosh, Jitender Saini, Madhura Ingalhalikar:
On Validating Multimodal MRI Based Stratification of IDH Genotype in High Grade Gliomas Using CNNs and Its Comparison to Radiomics. 53-60 - Saima Rathore, Ahmad Chaddad, Nadeem Haider Bukhari, Tamim Niazi:
Imaging Signature of 1p/19q Co-deletion Status Derived via Machine Learning in Lower Grade Glioma. 61-69 - Zhenwei Shi, Chong Zhang, Inge Compter, Maikel Verduin, Ann Hoeben, Danielle Eekers, Andre Dekker, Leonard Wee:
A Feature-Pooling and Signature-Pooling Method for Feature Selection for Quantitative Image Analysis: Application to a Radiomics Model for Survival in Glioma. 70-80 - Zhiyuan Xue, Bowen Xin, Dingqian Wang, Xiuying Wang:
Radiomics-Enhanced Multi-task Neural Network for Non-invasive Glioma Subtyping and Segmentation. 81-90
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