Optimizing Neuro-Oncology Imaging: A Review of Deep Learning Approaches for Glioma Imaging

Optimizing Neuro-Oncology Imaging: A Review of Deep Learning Approaches for Glioma Imaging

Radiographic assessment with magnetic resonance imaging (MRI) is widely used to characterize gliomas, which represent 80% of all primary malignant brain tumors. Unfortunately, glioma biology is marked by heterogeneous angiogenesis, cellular proliferation, cellular invasion, and apoptosis. This trans...

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Bibliographic Details
Main Authors: Madeleine M. Shaver, Paul A. Kohanteb, Catherine Chiou, Michelle D. Bardis, Chanon Chantaduly, Daniela Bota, Christopher G. Filippi, Brent Weinberg, Jack Grinband, Daniel S. Chow, Peter D. Chang
Format: Article
Language:English
Published: MDPI AG 2019-06-01
Series:Cancers
Subjects:
glioma
glioblastoma
machine learning
artificial intelligence
deep learning
neural network
Online Access:https://www.mdpi.com/2072-6694/11/6/829

Internet

https://www.mdpi.com/2072-6694/11/6/829

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