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Multi-auxiliary domain transfer learning for diagnosis of MCI conversion
Neurological Sciences ( IF 2.7 ) Pub Date : 2021-09-12 , DOI: 10.1007/s10072-021-05568-6
Bo Cheng 1, 2 , Bingli Zhu 2 , Shuchang Pu 3
Affiliation  

In the early stage of Alzheimer’s disease (AD), mild cognitive impairment (MCI) has a higher risk of progression to AD, so the prediction of whether an MCI subject will progress to AD (known as progressive MCI, PMCI) or not (known as stable MCI, SMCI) within a certain period is particularly important in practice. It is known that such a task could benefit from jointly learning-related auxiliary tasks such as differentiating AD from PMCI or PMCI from normal control (NC) in order to take full advantage of their shared commonality. However, few existing methods along this line fully consider the correlations between the target and auxiliary tasks according to the clinical practice of AD pathology for diagnosis. To deal with this problem, in this paper, treating each task domain as a different one, we borrow the idea from transfer learning and propose a novel multi-auxiliary domain transfer learning (MaDTL) method, which explicitly utilizes the correlations between the target domain (task) and multi-auxiliary domains (tasks) according to the clinical practice. Specifically, the proposed MaDTL method incorporates two key modules. The first one is a multi-auxiliary domain transfer-based feature selection (MaDTFS) model, which can select a discriminative feature subset shared by the target domain and the multi-auxiliary domains. In the MaDTFS model, to combine more training data from multi-auxiliary domains and simultaneously suppress the negative effects resulting from the irrelevant parts of multi-auxiliary domains, we proposed a sparse group correlation Lasso that includes a proposed group correlation Lasso penalty (i.e., \({\Vert \mathbf{W}\mathbf{H}\Vert }_{\mathrm{2,1}}\)) and a proposed correlation Lasso penalty (i.e., \({\Vert \mathbf{W}\mathbf{H}\Vert }_{\mathrm{1,1}}\)). The second module in MaDTL is a multi-auxiliary domain transfer-based classification (MaDTC) model that improves the voting with linear weighting-based ensemble learning. This model extends the constraints of the linear weighting method so that it can simultaneously combine training data from multi-auxiliary domains and achieve a robust classifier by minimizing negative effects from the irrelevant part of multi-auxiliary domains. Experimental results on 409 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database with the baseline magnetic resonance imaging (MRI) and cerebrospinal fluid (CSF) data validate the effectiveness of the proposed method by significantly improving the classification accuracy to 80.37% for the identification of MCI-to-AD conversion, outperforming the state-of-the-art methods.



中文翻译:

用于诊断 MCI 转换的多辅助域迁移学习

在阿尔茨海默病(AD)的早期阶段,轻度认知障碍(MCI)发展为AD的风险较高,因此预测一个MCI受试者是否会发展为AD(称为progressive MCI,PMCI)与否(已知作为稳定的MCI,SMCI)在一定时期内的实践尤为重要。众所周知,这样的任务可以受益于联合学习相关的辅助任务,例如将 AD 与 PMCI 或 PMCI 与正常控制 (NC) 区分开来,以充分利用它们的共同点。然而,很少有沿线的现有方法根据AD病理学的临床实践充分考虑目标和辅助任务之间的相关性进行诊断。为了解决这个问题,在本文中,将每个任务域视为一个不同的域,我们借鉴了迁移学习的思想,提出了一种新颖的多辅助域迁移学习(MaDTL)方法,该方法根据临床实践明确利用了目标域(任务)和多辅助域(任务)之间的相关性。具体来说,所提出的 MaDTL 方法包含两个关键模块。第一个是基于多辅助域迁移的特征选择(MaDTFS)模型,可以选择目标域和多辅助域共享的判别特征子集。在 MaDTFS 模型中,为了结合来自多辅助域的更多训练数据并同时抑制多辅助域的不相关部分造成的负面影响,我们提出了一个稀疏组相关 Lasso,其中包括一个提议的组相关 Lasso 惩罚(即,它根据临床实践明确利用目标域(任务)和多辅助域(任务)之间的相关性。具体来说,所提出的 MaDTL 方法包含两个关键模块。第一个是基于多辅助域迁移的特征选择(MaDTFS)模型,可以选择目标域和多辅助域共享的判别特征子集。在 MaDTFS 模型中,为了结合来自多辅助域的更多训练数据并同时抑制多辅助域的不相关部分造成的负面影响,我们提出了一个稀疏组相关 Lasso,其中包括一个提议的组相关 Lasso 惩罚(即,它根据临床实践明确利用目标域(任务)和多辅助域(任务)之间的相关性。具体来说,所提出的 MaDTL 方法包含两个关键模块。第一个是基于多辅助域迁移的特征选择(MaDTFS)模型,可以选择目标域和多辅助域共享的判别特征子集。在 MaDTFS 模型中,为了结合来自多辅助域的更多训练数据并同时抑制多辅助域的不相关部分造成的负面影响,我们提出了一个稀疏组相关 Lasso,其中包括一个提议的组相关 Lasso 惩罚(即,所提出的 MaDTL 方法包含两个关键模块。第一个是基于多辅助域迁移的特征选择(MaDTFS)模型,可以选择目标域和多辅助域共享的判别特征子集。在 MaDTFS 模型中,为了结合来自多辅助域的更多训练数据并同时抑制多辅助域的不相关部分造成的负面影响,我们提出了一个稀疏组相关 Lasso,其中包括一个提议的组相关 Lasso 惩罚(即,所提出的 MaDTL 方法包含两个关键模块。第一个是基于多辅助域迁移的特征选择(MaDTFS)模型,可以选择目标域和多辅助域共享的判别特征子集。在 MaDTFS 模型中,为了结合来自多辅助域的更多训练数据并同时抑制多辅助域的不相关部分造成的负面影响,我们提出了一个稀疏组相关 Lasso,其中包括一个提议的组相关 Lasso 惩罚(即,\({\Vert \mathbf{W}\mathbf{H}\Vert }_{\mathrm{2,1}}\))和建议的相关 Lasso 惩罚(即\({\Vert \mathbf{W} \mathbf{H}\Vert }_{\mathrm{1,1}}\))。MaDTL 中的第二个模块是基于多辅助域转移的分类 (MaDTC) 模型,该模型通过基于线性加权的集成学习改进了投票。该模型扩展了线性加权方法的约束,使其可以同时组合来自多个辅助域的训练数据,并通过最小化来自多个辅助域的不相关部分的负面影响来实现鲁棒的分类器。来自阿尔茨海默病神经影像学倡议 (ADNI) 数据库的 409 名受试者的实验结果具有基线磁共振成像 (MRI) 和脑脊液 (CSF) 数据,通过将分类准确度显着提高到 80.37% 来验证所提出方法的有效性。 MCI 到 AD 的转换,优于最先进的方法。

更新日期:2021-09-13
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