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首页> 外文期刊>Clinical Radiology: Journal of the Royal College of Radiologists >Deep learning can see the unseeable: predicting molecular markers from MRI of brain gliomas
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Deep learning can see the unseeable: predicting molecular markers from MRI of brain gliomas

机译:深度学习可以看到可疑的:预测来自脑胶质瘤的MRI的分子标记

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This paper describes state-of-the-art methods for molecular biomarker prediction utilising magnetic resonance imaging. This review paper covers both classical machine learning approaches and deep learning approaches to identifying the predictive features and to perform the actual prediction. In particular, there have been substantial advances in recent years in predicting molecular markers for diffuse gliomas. There are few examples of molecular marker prediction for other brain tumours. Deep learning has contributed significantly to these advances, but suffers from challenges in identifying the features used to make predictions. Tools to better identify and understand those features represent an important area of active research. (C) 2019 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.
机译:本文描述了利用磁共振成像的分子生物标志物预测的最先进方法。 本综述纸张涵盖了古典机器学习方法和深度学习方法,以识别预测特征和执行实际预测。 特别是,近年来在预测弥漫性胶质瘤的分子标志物中存在显着的进步。 对于其他脑肿瘤,少数分子标记预测的例子。 深度学习对这些进步作出了重大贡献,但遭遇识别用于预测的特征的挑战。 更好地识别和理解这些功能的工具代表了积极研究的重要领域。 (c)2019年皇家放射科医生。 elsevier有限公司出版。保留所有权利。

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