Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention : International Workshops, LABELS 2019, HAL-MICCAI 2019, and CuRIOUS 2019, held in c...最新文献

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Data Augmentation Based on Substituting Regional MRIs Volume Scores. 基于区域磁共振成像体积分数替代的数据扩增

Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention : International Workshops, LABELS 2019, HAL-MICCAI 2019, and CuRIOUS 2019, held in c...

Pub Date : 2019-10-01 Epub Date: 2019-10-24 DOI: 10.1007/978-3-030-33642-4_4

Tuo Leng, Qingyu Zhao, Chao Yang, Zhufu Lu, Ehsan Adeli, Kilian M Pohl

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 on regional 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.

由于难以收集到足够的训练数据，基于神经网络的方法在脑部磁共振成像（MRI）分析方面的最新进展尚未得到充分探索。解决数据有限问题的一个可行办法是用合成数据来增加训练集。在本文中，我们提出了一种基于区域特征替换的数据增强策略。我们展示了这一策略在训练基于神经网络的简单分类器方面的优势，该分类器可以仅根据青少年的核磁共振成像容积测量结果预测他们何时从不善饮酒者过渡到中重度饮酒者。在 20 倍交叉验证的基础上，我们在每次训练运行中从不到 500 名受试者中生成了超过一百万个合成样本。分类器在正确区分基线非饮酒者和饮酒者方面的准确率为 74.1%，在预测三年内的转变方面的加权准确率为 43.2%（5 组分类任务）。这两个准确率都明显高于在原始数据集上训练分类器的结果。

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