论文标题
基于结构MRI的自闭症谱系障碍分类中的元数据研究
Meta-data Study in Autism Spectrum Disorder Classification Based on Structural MRI
论文作者
论文摘要
基于神经影像数据的自闭症谱系障碍（ASD）的准确诊断具有重要意义，因为从ASD检测中提取有用的神经影像学数据很具有挑战性。即使已经利用了机器学习技术来改善神经影像数据中的信息提取，但不同的元数据条件（即数据收集策略）引起的数据质量的变化限制了可以提取的有效信息，从而导致数据依赖于数据的ASD检测中的预测精确性，而在某些情况下，这可能是更糟的。在这项工作中，我们系统地研究了三种元数据对基于从20个不同位点收集的结构MRI分类的预测准确性的影响，其中元数据条件有所不同。
Accurate diagnosis of autism spectrum disorder (ASD) based on neuroimaging data has significant implications, as extracting useful information from neuroimaging data for ASD detection is challenging. Even though machine learning techniques have been leveraged to improve the information extraction from neuroimaging data, the varying data quality caused by different meta-data conditions (i.e., data collection strategies) limits the effective information that can be extracted, thus leading to data-dependent predictive accuracies in ASD detection, which can be worse than random guess in some cases. In this work, we systematically investigate the impact of three kinds of meta-data on the predictive accuracy of classifying ASD based on structural MRI collected from 20 different sites, where meta-data conditions vary.
