IEEE Trans Neural Netw Learn Syst. 2020 Jan;31(1):186-200. doi: 10.1109/TNNLS.2019.2900077. Epub 2019 Mar 20.
As the population becomes older worldwide, accurate computer-aided diagnosis for Alzheimer's disease (AD) in the early stage has been regarded as a crucial step for neurodegeneration care in recent years. Since it extracts the low-level features from the neuroimaging data, previous methods regarded this computer-aided diagnosis as a classification problem that ignored latent featurewise relation. However, it is known that multiple brain regions in the human brain are anatomically and functionally interlinked according to the current neuroscience perspective. Thus, it is reasonable to assume that the extracted features from different brain regions are related to each other to some extent. Also, the complementary information between different neuroimaging modalities could benefit multimodal fusion. To this end, we consider leveraging the coupled interactions in the feature level and modality level for diagnosis in this paper. First, we propose capturing the feature-level coupled interaction using a coupled feature representation. Then, to model the modality-level coupled interaction, we present two novel methods: 1) the coupled boosting (CB) that models the correlation of pairwise coupled-diversity on both inconsistently and incorrectly classified samples between different modalities and 2) the coupled metric ensemble (CME) that learns an informative feature projection from different modalities by integrating the intrarelation and interrelation of training samples. We systematically evaluated our methods with the AD neuroimaging initiative data set. By comparison with the baseline learning-based methods and the state-of-the-art methods that are specially developed for AD/MCI (mild cognitive impairment) diagnosis, our methods achieved the best performance with accuracy of 95.0% and 80.7% (CB), 94.9% and 79.9% (CME) for AD/NC (normal control), and MCI/NC identification, respectively.
随着全球人口老龄化，近年来，准确的计算机辅助诊断阿尔茨海默病（AD）已被视为神经退行性疾病护理的关键步骤。由于它从神经影像学数据中提取低级特征，因此以前的方法将这种计算机辅助诊断视为忽略潜在特征关系的分类问题。然而，根据当前的神经科学观点，已知人脑的多个大脑区域在解剖和功能上是相互关联的。因此，可以合理地假设从不同大脑区域提取的特征在某种程度上是相互关联的。此外，不同神经影像学模式之间的互补信息可以受益于多模态融合。为此，我们考虑在本文中利用特征级和模式级的耦合交互作用进行诊断。首先，我们提出使用耦合特征表示来捕获特征级耦合交互作用。然后，为了建模模式级耦合交互作用，我们提出了两种新方法：1）耦合提升（CB），该方法在不同模式之间的不一致和错误分类样本上对成对耦合多样性进行建模；2）耦合度量集成（CME），该方法通过整合训练样本的内部关系和相互关系，从不同模式中学习信息丰富的特征投影。我们使用 AD 神经影像学倡议数据集系统地评估了我们的方法。通过与基于学习的基线方法和专门为 AD/MCI（轻度认知障碍）诊断开发的最新方法进行比较，我们的方法在 AD/NC（正常对照）和 MCI/NC 识别方面分别取得了 95.0%和 80.7%（CB）和 94.9%和 79.9%（CME）的最佳性能。
