论文标题
基于张量的多模式特征选择和阿尔茨海默氏病诊断的回归
Tensor-Based Multi-Modality Feature Selection and Regression for Alzheimer's Disease Diagnosis
论文作者
论文摘要
与大脑变化相关的阿尔茨海默氏病（AD）和轻度认知障碍（MCI）的评估仍然是一项艰巨的任务。最近的研究表明，多模式成像技术的组合可以更好地反映病理特征，并有助于更准确地诊断AD和MCI。在本文中，我们提出了一种新型的基于张量的多模式特征选择和回归方法，用于诊断和生物标志物对正常对照的AD和MCI鉴定。具体而言，我们利用张量结构来利用多模式数据中固有的高级相关信息，并研究多线性回归模型中的张量级稀疏性。我们使用三种成像方式（VBM-MRI，FDG-PET和AV45-PET）具有疾病严重程度和认知评分的临床参数来分析ADNI数据的方法的实际优势。实验结果表明，我们提出的方法的优越性与疾病诊断的最新方法以及疾病特异性区域的鉴定和与模态相关的差异相比。这项工作的代码可在https://github.com/junfish/bios22上公开获得。
The assessment of Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) associated with brain changes remains a challenging task. Recent studies have demonstrated that combination of multi-modality imaging techniques can better reflect pathological characteristics and contribute to more accurate diagnosis of AD and MCI. In this paper, we propose a novel tensor-based multi-modality feature selection and regression method for diagnosis and biomarker identification of AD and MCI from normal controls. Specifically, we leverage the tensor structure to exploit high-level correlation information inherent in the multi-modality data, and investigate tensor-level sparsity in the multilinear regression model. We present the practical advantages of our method for the analysis of ADNI data using three imaging modalities (VBM- MRI, FDG-PET and AV45-PET) with clinical parameters of disease severity and cognitive scores. The experimental results demonstrate the superior performance of our proposed method against the state-of-the-art for the disease diagnosis and the identification of disease-specific regions and modality-related differences. The code for this work is publicly available at https://github.com/junfish/BIOS22.
