2019
DOI: 10.2214/ajr.18.20218
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State of the Art: Machine Learning Applications in Glioma Imaging

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Cited by 85 publications
(54 citation statements)
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References 63 publications
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“…Traditional machine‐learning algorithms were initially proposed to be used for the classification of medical images . Such algorithms might provide time‐saving and objective evaluation, for example, in multimodal studies comprising 1 H and X‐nuclei imaging.…”
Section: Latest Developments and Future Directionsmentioning
confidence: 99%
“…Traditional machine‐learning algorithms were initially proposed to be used for the classification of medical images . Such algorithms might provide time‐saving and objective evaluation, for example, in multimodal studies comprising 1 H and X‐nuclei imaging.…”
Section: Latest Developments and Future Directionsmentioning
confidence: 99%
“…Lotan used automatic segmentation methods to obtain the tumor contours and areas, just like in our delineation. The following classification also depends on machine learning techniques [30]. Another study used diagnostic information from multiple modalities to achieve better performance [39].…”
Section: Discussionmentioning
confidence: 99%
“…Yang et al used DCNN to classify 113 gliomas and achieved good accuracy [29]. Lotan used machine learning techniques to classify tumors based on the features extracted from image segmentation [30].…”
Section: Introductionmentioning
confidence: 99%
“…In recent years, utilizing machine learning (ML) methods for characterizing gliomas from medical imaging have attracted attention [11]. With regards to predicting glioma characteristics from MRI radiomic features, studies have primarily explored support vector machines (SVM) and random forest (RF) classifiers [11,12]. Recently, a new open source highly scalable gradient tree boosting model named eXtreme Gradient Boosting (XGBoost) has been introduced with some promising results [13].…”
Section: Introductionmentioning
confidence: 99%