当前神经影像中的深度学习：一种具有功效和I类错误控制但泛化能力存疑的多变量方法
Deep Learning in current Neuroimaging: a multivariate approach with power and type I error control but arguable generalization ability
AI总结：
本文针对神经影像深度学习分类的统计显著性问题，提出基于标签置换检验的非参数框架，结合自编码器与支持向量机，并采用RUB方法以较低计算成本改善交叉验证下的泛化能力不足问题。
摘要：
在神经影像中，借助深度/机器学习技术进行的判别分析通常使用验证技术进行测试，而与之相关的统计显著性由于其计算复杂性在很大程度上仍未得到充分发展。在这项工作中，提出了一个非参数框架，用于估计使用深度学习架构进行分类的统计显著性。具体而言，将自编码器（AE）和支持向量机（SVM）的组合应用于：（i）单条件、组内设计，通常为正常对照（NC）；以及（ii）双条件、组间设计，例如将阿尔茨海默病（AD）患者与NC进行对比（还包括向多类分析的扩展）。在这两项研究中，提出了一种基于标签置换检验的随机效应推断，使用交叉验证（CV）和带上限校正的重代入（RUB）作为验证方法。这使得既能检测假阳性和分类器过拟合，也能估计检验的统计功效。使用阿尔茨海默病神经影像倡议（ADNI）数据集、显性遗传阿尔茨海默网络（DIAN）数据集以及一个MCI预测数据集进行了若干实验。我们在置换检验中发现，CV和RUB方法提供的假阳性率接近显著性水平，并具有可接受的统计功效（尽管使用交叉验证时较低）。使用CV观察到训练准确率和测试准确率之间存在较大差距，尤其是在单条件设计中。这意味着泛化能力较低，因为训练中拟合的模型相对于测试集不具有信息性。我们提出通过应用RUB作为解决方案，由此获得与CV测试集相似的结果，但考虑了整个集合，并且每次迭代的计算成本更低。
英文摘要：
Discriminative analysis in neuroimaging by means of deep/machine learning techniques is usually tested with validation techniques, whereas the associated statistical significance remains largely under-developed due to their computational complexity. In this work, a non-parametric framework is proposed that estimates the statistical significance of classifications using deep learning architectures. In particular, a combination of autoencoders (AE) and support vector machines (SVM) is applied to: (i) a one-condition, within-group designs often of normal controls (NC) and; (ii) a two-condition, between-group designs which contrast, for example, Alzheimer's disease (AD) patients with NC (the extension to multi-class analyses is also included). A random-effects inference based on a label permutation test is proposed in both studies using cross-validation (CV) and resubstitution with upper bound correction (RUB) as validation methods. This allows both false positives and classifier overfitting to be detected as well as estimating the statistical power of the test. Several experiments were carried out using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, the Dominantly Inherited Alzheimer Network (DIAN) dataset, and a MCI prediction dataset. We found in the permutation test that CV and RUB methods offer a false positive rate close to the significance level and an acceptable statistical power (although lower using cross-validation). A large separation between training and test accuracies using CV was observed, especially in one-condition designs. This implies a low generalization ability as the model fitted in training is not informative with respect to the test set. We propose as solution by applying RUB, whereby similar results are obtained to those of the CV test set, but considering the whole set and with a lower computational cost per iteration.
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