Machine Learning Models for Multiparametric Glioma Grading With Quantitative Result Interpretations

Machine Learning Models for Multiparametric Glioma Grading With Quantitative Result Interpretations

Gliomas are the most common primary malignant brain tumors in adults. Accurate grading is crucial as therapeutic strategies are often disparate for different grades and may influence patient prognosis. This study aims to provide an automated glioma grading platform on the basis of machine learning m...

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Bibliographic Details
Main Authors: Xiuying Wang, Dingqian Wang, Zhigang Yao, Bowen Xin, Bao Wang, Chuanjin Lan, Yejun Qin, Shangchen Xu, Dazhong He, Yingchao Liu
Format: Article
Language:English
Published: Frontiers Media S.A. 2019-01-01
Series:Frontiers in Neuroscience
Subjects:
glioma grading
machine learning
morphological features
support vector machine
digital pathology images
Online Access:https://www.frontiersin.org/article/10.3389/fnins.2018.01046/full

Internet

https://www.frontiersin.org/article/10.3389/fnins.2018.01046/full

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