Relation-Induced Multi-Modal Shared Representation Learning for Alzheimer’s Disease Diagnosis
Relation-Induced Multi-Modal Shared Representation Learning for Alzheimer’s Disease Diagnosis
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DOI：
10.1109/tmi.2021.3063150
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发表时间：
2021-03
影响因子：
10.6
通讯作者：
Zhenyuan Ning;Qing Xiao;Qianjin Feng;Wufan Chen;Yu Zhang
中科院分区：
文献类型：
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作者：
Zhenyuan Ning;Qing Xiao;Qianjin Feng;Wufan Chen;Yu Zhang
多模态数据的融合（例如，磁共振成像（MRI）和正电子发射断层摄影术（PET））通过提供互补的结构和功能信息而被普遍用于精确识别阿尔茨海默病（AD）。然而，大多数现有的方法简单地连接多模态特征在原始空间中，并忽略其潜在的关联，这可能会提供更多的判别特征的AD识别。同时，如何克服高维多模态数据带来的过拟合问题也是一个亟待解决的问题。为此，我们提出了一种关系诱导的多模态共享表示学习方法用于AD诊断。该方法集成了表示学习，降维，分类器建模到一个统一的框架。具体来说，该框架首先通过学习原始空间和共享空间之间的双向映射来获得多模态共享表示。在这个共享空间中，我们利用几个关系正则化器（包括特征-特征，特征-标签和样本-样本正则化器）和辅助正则化器来鼓励学习多模态数据中固有的潜在关联并分别减轻过拟合。接下来，我们将共享表示投影到AD诊断的目标空间中。为了验证我们提出的方法的有效性，我们在两个独立的数据集上进行了广泛的实验（即，ADNI-1和ADNI-2），实验结果表明，我们提出的方法优于几个国家的最先进的方法。
The fusion of multi-modal data (e.g., magnetic resonance imaging (MRI) and positron emission tomography (PET)) has been prevalent for accurate identification of Alzheimer’s disease (AD) by providing complementary structural and functional information. However, most of the existing methods simply concatenate multi-modal features in the original space and ignore their underlying associations which may provide more discriminative characteristics for AD identification. Meanwhile, how to overcome the overfitting issue caused by high-dimensional multi-modal data remains appealing. To this end, we propose a relation-induced multi-modal shared representation learning method for AD diagnosis. The proposed method integrates representation learning, dimension reduction, and classifier modeling into a unified framework. Specifically, the framework first obtains multi-modal shared representations by learning a bi-directional mapping between original space and shared space. Within this shared space, we utilize several relational regularizers (including feature-feature, feature-label, and sample-sample regularizers) and auxiliary regularizers to encourage learning underlying associations inherent in multi-modal data and alleviate overfitting, respectively. Next, we project the shared representations into the target space for AD diagnosis. To validate the effectiveness of our proposed approach, we conduct extensive experiments on two independent datasets (i.e., ADNI-1 and ADNI-2), and the experimental results demonstrate that our proposed method outperforms several state-of-the-art methods.