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首页> 外文期刊>Medical image analysis >Detecting outliers in high-dimensional neuroimaging datasets with robust covariance estimators
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Detecting outliers in high-dimensional neuroimaging datasets with robust covariance estimators

机译：使用鲁棒协方差估计器检测高维神经影像数据集中的异常值

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摘要

Medical imaging datasets often contain deviant observations, the so-called outliers, due to acquisition or preprocessing artifacts or resulting from large intrinsic inter-subject variability. These can undermine the statistical procedures used in group studies as the latter assume that the cohorts are composed of homogeneous samples with anatomical or functional features clustered around a central mode. The effects of outlying subjects can be mitigated by detecting and removing them with explicit statistical control. With the emergence of large medical imaging databases, exhaustive data screening is no longer possible, and automated outlier detection methods are currently gaining interest. The datasets used in medical imaging are often high-dimensional and strongly correlated. The outlier detection procedure should therefore rely on high-dimensional statistical multivariate models. However, state-of-the-art procedures, based on the Minimum Covariance Determinant (MCD) estimator, are not well-suited for such high-dimensional settings. In this work, we introduce regularization in the MCD framework and investigate different regularization schemes. We carry out extensive simulations to provide backing for practical choices in absence of ground truth knowledge. We demonstrate on functional neuroimaging datasets that outlier detection can be performed with small sample sizes and improves group studies.
机译：由于采集或预处理伪像或由于较大的内部受试者间差异而导致的医学影像数据集通常包含异常观测值，即所谓的异常值。这些可能会破坏小组研究中使用的统计程序，因为后者假设该队列由具有围绕中心模式聚集的解剖或功能特征的均质样本组成。可以通过使用显式统计控制来检测和删除偏远主题来减轻其影响。随着大型医学影像数据库的出现，详尽的数据筛选已不再可能，并且自动异常值检测方法目前也引起了人们的兴趣。医学成像中使用的数据集通常是高维的且高度相关。因此，异常值检测过程应依赖于高维统计多元模型。但是，基于最小协方差决定因素（MCD）估算器的最新程序并不适合这种高维设置。在这项工作中，我们在MCD框架中引入正则化并研究不同的正则化方案。我们进行了广泛的模拟，以在没有基础事实的情况下为实际选择提供支持。我们在功能性神经影像数据集上证明，可以用小样本量执行异常检测，并改善了小组研究。

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