Updates on Deep Learning and Glioma: Use of Convolutional Neural Networks to Image Glioma Heterogeneity.

Clicks: 309
ID: 128175
2020
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Ranked #4 of 9 articles by views in neuroimaging clinics of north america

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Abstract
Deep learning represents end-to-end machine learning in which feature selection from images and classification happen concurrently. This articles provides updates on how deep learning is being applied to the study of glioma and its genetic heterogeneity. Deep learning algorithms can detect patterns in routine and advanced MR imaging that elude the eyes of neuroradiologists and make predictions about glioma genetics, which impact diagnosis, treatment response, patient management, and long-term survival. The success of these deep learning initiatives may enhance the performance of neuroradiologists and add greater value to patient care by expediting treatment.
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chow2020updatesneuroimaging Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Chow, Daniel S;Khatri, Deepak;Chang, Peter D;Zlochower, Avraham;Boockvar, John A;Filippi, Christopher G;
Journal neuroimaging clinics of north america
Year 2020
DOI
S1052-5149(20)30052-6
URL
Keywords
Deep learning convolutional neural networks brain tumor glioblastoma multiforme

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