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

Machine Learning (832) Artificial Intelligence (498) Imaging Informatics (125)

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 Gao 1 Author Orcid Image ;   Xiong Xiao 2 Author Orcid Image ;   Bangcheng Han 3 Author Orcid Image ;   Guilin Li 4 Author Orcid Image ;   Xiaolin Ning 3 Author Orcid Image ;   Defeng Wang 3 Author Orcid Image ;   Weidong Cai 5 Author Orcid Image ;   Ron Kikinis 6, 7, 8 Author Orcid Image ;   Shlomo Berkovsky 9 Author Orcid Image ;   Antonio Di Ieva 10 Author Orcid Image ;   Liwei Zhang 2 Author Orcid Image ;   Nan Ji 2 Author Orcid Image ;   Sidong Liu 9 Author Orcid Image
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Journals

  1. Liu S, Meng T, Russo C, Di Ieva A, Berkovsky S, Peng L, Dou W, Qian L. Brain volumetric and fractal analysis of synthetic MRI: A comparative study with conventional 3D T1-weighted images. European Journal of Radiology 2021;141:109782 View