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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

  • Conference proceedings
  • © 2020

Overview

Editors:
  1. Hassan Mohy-ud-Din

    1. LUMS School of Science and Engineering, Lahore, Pakistan

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    You can also search for this editor in PubMed   Google Scholar

  2. Saima Rathore

    1. University of Pennsylvania, Philadelphia, USA

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    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 11991)

Part of the book sub series: Image Processing, Computer Vision, Pattern Recognition, and Graphics (LNIP)

Included in the following conference series:

Conference proceedings info: RNO-AI 2019.

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About this book

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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Keywords

Table of contents (9 papers)

  1. Front Matter

    Pages i-ix
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  2. Current Status of the Use of Machine Learning and Magnetic Resonance Imaging in the Field of Neuro-Radiomics

    • Ashish Singh, Michel Bilello
    Pages 1-11

  3. Opportunities and Advances in Radiomics and Radiogenomics in Neuro-Oncology

    • Kaustav Bera, Niha Beig, Pallavi Tiwari
    Pages 12-23

  4. A Survey on Recent Advancements for AI Enabled Radiomics in Neuro-Oncology

    • Syed Muhammad Anwar, Tooba Altaf, Khola Rafique, Harish RaviPrakash, Hassan Mohy-ud-Din, Ulas Bagci
    Pages 24-35

  5. Deep Radiomic Features from MRI Scans Predict Survival Outcome of Recurrent Glioblastoma

    • Ahmad Chaddad, Mingli Zhang, Christian Desrosiers, Tamim Niazi
    Pages 36-43

  6. cuRadiomics: A GPU-Based Radiomics Feature Extraction Toolkit

    • Yining Jiao, Oihane Mayo Ijurra, Lichi Zhang, Dinggang Shen, Qian Wang
    Pages 44-52

  7. On Validating Multimodal MRI Based Stratification of IDH Genotype in High Grade Gliomas Using CNNs and Its Comparison to Radiomics

    • Tanay Chougule, Sumeet Shinde, Vani Santosh, Jitender Saini, Madhura Ingalhalikar
    Pages 53-60

  8. Imaging Signature of 1p/19q Co-deletion Status Derived via Machine Learning in Lower Grade Glioma

    • Saima Rathore, Ahmad Chaddad, Nadeem Haider Bukhari, Tamim Niazi
    Pages 61-69

  9. A Feature-Pooling and Signature-Pooling Method for Feature Selection for Quantitative Image Analysis: Application to a Radiomics Model for Survival in Glioma

    • Zhenwei Shi, Chong Zhang, Inge Compter, Maikel Verduin, Ann Hoeben, Danielle Eekers et al.
    Pages 70-80

  10. Radiomics-Enhanced Multi-task Neural Network for Non-invasive Glioma Subtyping and Segmentation

    • Zhiyuan Xue, Bowen Xin, Dingqian Wang, Xiuying Wang
    Pages 81-90

  11. Back Matter

    Pages 91-91
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Other volumes

  1. Radiomics and Radiogenomics in Neuro-oncology

Editors and Affiliations

  • LUMS School of Science and Engineering, Lahore, Pakistan

    Hassan Mohy-ud-Din

  • University of Pennsylvania, Philadelphia, USA

    Saima Rathore

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