• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Leveraging Coupled Interaction for Multimodal Alzheimer’s Disease Diagnosis
【24h】

Leveraging Coupled Interaction for Multimodal Alzheimer’s Disease Diagnosis

机译：利用耦合相互作用进行多模式阿尔茨海默氏病诊断

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

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.
机译：随着全世界人口的老龄化，近年来，在早期对阿尔茨海默氏病（AD）进行准确的计算机辅助诊断已被视为神经退行性疾病治疗的关键步骤。由于它从神经影像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，已知的是，根据当前的神经科学观点，人脑中的多个脑区域在解剖学上和功能上是互连的。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彼此相关。同样，不同的神经影像学模态之间的互补信息可能有益于多模态融合。为此，在本文中，我们考虑利用特征级别和模态级别的耦合交互进行诊断。首先，我们建议使用耦合特征表示来捕获特征级耦合交互。然后，为了建模模态级别的耦合交互，我们提出了两种新颖的方法：1）耦合升压（CB），该模型对不同模态之间不一致和错误分类的样本上的成对耦合多样性进行建模，以及2）耦合度量集成（CME），它通过集成训练样本的内部关系和内部关系从不同的模式中学习信息丰富的特征投影。我们使用AD神经影像主动数据集系统地评估了我们的方法。与基于基线学习的方法和专为AD / MCI（轻度认知障碍）诊断而开发的最新方法相比，我们的方法以95.0％和80.7％（CB）的准确性达到了最佳性能。 ），AD / NC（正常对照）和MCI / NC识别分别为94.9％和79.9％（CME）。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号