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首页> 外文期刊>Medical image analysis >Multi-modal latent space inducing ensemble SVM classifier for early dementia diagnosis with neuroimaging data
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Multi-modal latent space inducing ensemble SVM classifier for early dementia diagnosis with neuroimaging data

机译：多模态潜空间诱导整体SVM分类器，用于具有神经影像数据的早期痴呆诊断

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摘要

Fusing multi-modality data is crucial for accurate identification of brain disorder as different modalities can provide complementary perspectives of complex neurodegenerative disease. However, there are at least four common issues associated with the existing fusion methods. First, many existing fusion methods simply concatenate features from each modality without considering the correlations among different modalities. Second, most existing methods often make prediction based on a single classifier, which might not be able to address the heterogeneity of the Alzheimer's disease (AD) progression. Third, many existing methods often employ feature selection (or reduction) and classifier training in two independent steps, without considering the fact that the two pipelined steps are highly related to each other. Forth, there are missing neuroimaging data for some of the participants (e.g., missing PET data), due to the participants' "no-show" or dropout. In this paper, to address the above issues, we propose an early AD diagnosis framework via novel multi-modality latent space inducing ensemble SVM classifier. Specifically, we first project the neuroimaging data from different modalities into a latent space, and then map the learned latent representations into the label space to learn multiple diversified classifiers. Finally, we obtain the more reliable classification results by using an ensemble strategy. More importantly, we present a Complete Multi-modality Latent Space (CMLS) learning model for complete multi-modality data and also an Incomplete Multi-modality Latent Space (IMLS) learning model for incomplete multi-modality data. Extensive experiments using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset have demonstrated that our proposed models outperform other state-of-the-art methods. (C) 2020 Elsevier B.V. All rights reserved.
机译：融合多模态数据对于精确鉴定脑障碍的敏感性，因为不同的方式可以提供复杂神经变性疾病的互补视角。然而，至少有四个与现有融合方法相关的常见问题。首先，许多现有的融合方法只需考虑不同模式之间的相关性，只需串联每个模式的功能。其次，大多数现有方法通常会基于单个分类器进行预测，这可能无法解决阿尔茨海默病（AD）进展的异质性。第三，许多现有方法通常采用两个独立步骤中的特征选择（或减少）和分类器训练，而不考虑两个流水线步骤彼此高度相关的事实。由于参与者的“无节目”或辍学，缺少一些参与者（例如，宠物数据）缺少神经影像数据的遗失数据。在本文中，为了解决上述问题，我们通过新型多种式潜空间诱导整体SVM分类器提出了早期的广告诊断框架。具体地，我们首先将来自不同模式的神经影像画数据投影到潜在空间中，然后将学习的潜在表示映射到标签空间中以学习多个多样化分类器。最后，我们通过使用集合策略获得更可靠的分类结果。更重要的是，我们为完整的多模态数据提供了一个完整的多模态潜空间（CMLS）学习模型，以及用于不完整的多模态数据的不完整的多模态潜空间（IML）学习模型。使用Alzheimer疾病的广泛实验神经影像倡议（ADNI）数据集已经证明我们所提出的模型优于其他最先进的方法。 （c）2020 Elsevier B.V.保留所有权利。

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