Data Augmentation Based on Substituting Regional MRIs Volume Scores.
Data Augmentation Based on Substituting Regional MRIs Volume Scores.
复制标题
基于替代区域 MRI 体积分数的数据增强。
DOI：
10.1007/978-3-030-33642-4_4
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Pohl,KilianM
中科院分区：
文献类型：
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作者：
Leng,Tuo;Zhao,Qingyu;Yang,Chao;Lu,Zhufu;Adeli,Ehsan;Pohl,KilianM
由于难以收集足够的训练数据，基于神经网络的方法的最新进展尚未在脑磁共振成像（MRI）分析中得到充分探讨。有限数据问题的一个可能的解决方案是用合成生成的数据来增加训练集。本文提出了一种基于区域特征替换的数据增强策略。我们证明了这种策略的优势，训练一个简单的基于神经网络的分类器，预测当个别青年过渡从没有到低到中度到重度饮酒者完全基于他们的体积MRI测量。基于20倍交叉验证，我们从不到500名受试者中为每次训练运行生成超过100万个合成样本。该分类器实现了74.1%的准确性，在正确区分非饮酒者从饮酒者在基线和43.2%的加权准确性，在预测过渡超过三年的时间（5组分类任务）。这两个准确度得分都明显优于在原始数据集上训练分类器。
Due to difficulties in collecting sufficient training data, recent advances in neural-network-based methods have not been fully explored in the analysis of brain Magnetic Resonance Imaging (MRI). A possible solution to the limited-data issue is to augment the training set with synthetically generated data. In this paper, we propose a data augmentation strategy based onregional feature substitution. We demonstrate the advantages of this strategy with respect to training a simple neural-network-based classifier in predicting when individual youth transition from no-to-low to medium-to-heavy alcohol drinkers solely based on their volumetric MRI measurements. Based on 20-fold cross-validation, we generate more than one million synthetic samples from less than 500 subjects for each training run. The classifier achieves an accuracy of 74.1% in correctly distinguishing non-drinkers from drinkers at baseline and a 43.2% weighted accuracy in predicting the transition over a three year period (5-group classification task). Both accuracy scores are significantly better than training the classifier on the original dataset.