Thung Kim-Han, Yap Pew-Thian, Adeli-M Ehsan, Shen Dinggang
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA.
Med Image Comput Comput Assist Interv. 2015 Oct;9351:527-534. doi: 10.1007/978-3-319-24574-4_63. Epub 2015 Nov 18.
Identifying progressive mild cognitive impairment (pMCI) patients and predicting when they will convert to Alzheimer's disease (AD) are important for early medical intervention. Multi-modality and longitudinal data provide a great amount of information for improving diagnosis and prognosis. But these data are often incomplete and noisy. To improve the utility of these data for prediction purposes, we propose an approach to denoise the data, impute missing values, and cluster the data into low-dimensional subspaces for pMCI prediction. We assume that the data reside in a space formed by a union of several low-dimensional subspaces and that similar MCI conditions reside in similar subspaces. Therefore, we first use incomplete low-rank representation (ILRR) and spectral clustering to cluster the data according to their representative low-rank subspaces. At the same time, we denoise the data and impute missing values. Then we utilize a low-rank matrix completion (LRMC) framework to identify pMCI patients and their time of conversion. Evaluations using the ADNI dataset indicate that our method outperforms conventional LRMC method.
识别轻度认知功能障碍（pMCI）患者并预测他们何时会转变为阿尔茨海默病（AD）对于早期医学干预至关重要。多模态和纵向数据为改善诊断和预后提供了大量信息。但这些数据往往不完整且有噪声。为了提高这些数据在预测方面的效用，我们提出了一种对数据进行去噪、插补缺失值并将数据聚类到低维子空间以进行pMCI预测的方法。我们假设数据存在于由几个低维子空间的并集形成的空间中，并且相似的MCI状况存在于相似的子空间中。因此，我们首先使用不完全低秩表示（ILRR）和谱聚类根据其代表性的低秩子空间对数据进行聚类。同时，我们对数据进行去噪并插补缺失值。然后我们利用低秩矩阵补全（LRMC）框架来识别pMCI患者及其转变时间。使用ADNI数据集进行的评估表明，我们的方法优于传统的LRMC方法。
