Deep learning reveals Alzheimer's disease onset in MCI subjects: Results from an international challenge
Deep learning reveals Alzheimer's disease onset in MCI subjects: Results from an international challenge
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DOI：
10.1016/j.jneumeth.2017.12.011
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发表时间：
2018-05-15
影响因子：
3
通讯作者：
Tangaro, Sabina
中科院分区：
文献类型：
--
作者：
Amoroso, Nicola;Diacono, Domenico;Tangaro, Sabina
背景资料：基于结构MRI特征的阿尔茨海默病（AD）的早期诊断及其在轻度认知障碍（MCI）受试者中的发病是神经影像学中最重要的开放性问题之一。因此，在国际Kaggle平台上发起了一项科学挑战，以评估不同分类方法预测MCI及其转化为AD的性能。本文提出了一种基于随机森林特征选择和深度神经网络分类的分类策略，该策略使用包括HC、AD、MCI和cMCI四类分类问题的混合队列，来训练模型。此外，我们比较这种方法与一种新的分类策略的基础上学习的模糊逻辑的混合队列，包括HC和AD。实验：240个科目的训练集和测试集，包括500个真实的和模拟科目的混合队列。数据包括AD患者、MCI受试者转化为AD（cMCI）、MCI受试者和健康对照（HC）。这项工作在19个参与团队中的总体准确率（38.8%）排名第三。与现有方法的比较：Kaggle平台主办的“从MRI数据自动预测MCI的国际挑战赛”已经推广，以使用一组通用的数据和评估程序来验证不同的方法。结论：DNN达到了明显高于其他机器学习策略的分类准确率;另一方面，模糊逻辑在cMCI中特别准确，这表明这些方法的组合可能会导致有趣的未来前景。(C)2017爱思唯尔B. V.保留所有权利。
Background: Early diagnosis of Alzheimer's disease (AD) and its onset in subjects affected by mild cognitive impairment (MCI) based on structural MRI features is one of the most important open issues in neuroimaging. Accordingly, a scientific challenge has been promoted, on the international Kaggle platform, to assess the performance of different classification methods for prediction of MCI and its conversion to AD.New method: This work presents a classification strategy based on Random Forest feature selection and Deep Neural Network classification using a mixed cohort including the four classes of classification problem, that is HC, AD, MCI and cMCI, to train the model. Moreover, we compare this approach with a novel classification strategy based on fuzzy logic learned on a mixed cohort including only HC and AD.Experiments: A training set of 240 subjects and a test set including mixed cohort of 500 real and simulated subjects were used. The data included AD patients, MCI subjects converting to AD (cMCI), MCI subjects and healthy controls (HC). This work ranked third for overall accuracy (38.8%) over 19 participating teams. Comparison with existing method(s): The "International challenge for automated prediction of MCI from MRI data" hosted by the Kaggle platform has been promoted to validate different methodologies with a common set of data and evaluation procedures.Conclusion: DNNs reach a classification accuracy significantly higher than other machine learning strategies; on the other hand, fuzzy logic is particularly accurate with cMCI, suggesting a combination of these approaches could lead to interesting future perspectives. (C) 2017 Elsevier B.V. All rights reserved.