• Neuroimaging Clin. N. Am. · Nov 2020

    Review

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

    • Daniel S Chow, Deepak Khatri, Peter D Chang, Avraham Zlochower, John A Boockvar, and Christopher G Filippi.
    • Department of Radiology, University of California-Irvine School of Medicine, Center for Artificial Intelligence in Diagnostic Medicine (CAIDM), 1001 Health Sciences Road, Orange, CA 92617, USA.
    • Neuroimaging Clin. N. Am. 2020 Nov 1; 30 (4): 493-503.

    AbstractDeep 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.Copyright © 2020 Elsevier Inc. All rights reserved.

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