This paper is in the following e-collection/theme issue:

Machine Learning (1401) Artificial Intelligence (1216) Imaging Informatics (187)

Published on in Vol 8, No 11 (2020): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/19805, first published .
Deep Learning Methodology for Differentiating Glioma Recurrence From Radiation Necrosis Using Multimodal Magnetic Resonance Imaging: Algorithm Development and Validation

Deep Learning Methodology for Differentiating Glioma Recurrence From Radiation Necrosis Using Multimodal Magnetic Resonance Imaging: Algorithm Development and Validation

Deep Learning Methodology for Differentiating Glioma Recurrence From Radiation Necrosis Using Multimodal Magnetic Resonance Imaging: Algorithm Development and Validation

Authors of this article:

Yang Gao1 Author Orcid Image ;   Xiong Xiao2 Author Orcid Image ;   Bangcheng Han3 Author Orcid Image ;   Guilin Li4 Author Orcid Image ;   Xiaolin Ning3 Author Orcid Image ;   Defeng Wang3 Author Orcid Image ;   Weidong Cai5 Author Orcid Image ;   Ron Kikinis6, 7, 8 Author Orcid Image ;   Shlomo Berkovsky9 Author Orcid Image ;   Antonio Di Ieva10 Author Orcid Image ;   Liwei Zhang2 Author Orcid Image ;   Nan Ji2 Author Orcid Image ;   Sidong Liu9 Author Orcid Image
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