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Ordinal Ranking for Detecting Mild Cognitive Impairment and Alzheimer's Disease Based on Multimodal Neuroimages and CSF Biomarkers

机译：基于多模式神经影像和CSF生物标记物检测轻度认知障碍和阿尔茨海默氏病的序数排名

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摘要

Early diagnosis of Alzheimer's disease (AD) based on neuroimaging and fluid biomarker data has attracted a lot of interest in medical image analysis. Most existing studies have been focusing on two-class classification problems, e.g., distinguishing AD patients from cognitive normal (CN) elderly or distinguishing mild cognitive impairment (MCI) individuals from CN elderly. However, to achieve the goal of early diagnosis of AD, we need to identify individuals with AD and MCI, especially MCI individuals who will convert to AD, in a single setting, which essentially is a multi-class classification problem. In this paper, we propose an ordinal ranking based classification method for distinguishing CN, MCI non-converter (MCI-NC), MCI converter (MCI-C), and AD at an individual level, taking into account the inherent ordinal severity of brain damage caused by normal aging, MCI, and AD, rather than formulating the classification as a multi-class classification problem. Experiment results indicate that the proposed method can achieve a better performance than traditional multi-class classification techniques based on multimodal neuroimaging and CSF biomarker data of the ADNI.
机译：基于神经影像和液体生物标志物数据的阿尔茨海默氏病（AD）的早期诊断引起了医学图像分析的极大兴趣。现有的大多数研究都集中在两类分类问题上，例如，将AD患者与认知正常（CN）老年人区分开来，或将轻度认知障碍（MCI）个体与CN老年人区分开来。但是，为了实现AD早期诊断的目标，我们需要在单个设置中识别具有AD和MCI的个体，尤其是将转换为AD的MCI个体，这本质上是一个多类分类问题。在本文中，我们提出了一种基于序数排序的分类方法，以便在考虑个体固有的序数严重性的情况下，对CN，MCI非转换器（MCI-NC），MCI转换器（MCI-C）和AD进行个体区分。由正常老化，MCI和AD造成的损坏，而不是将分类公式化为多分类问题。实验结果表明，与基于ADNI的多模态神经影像和CSF生物标记数据的传统多分类方法相比，该方法具有更好的性能。

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