Domain Transfer Learning for MCI Conversion Prediction.
Domain Transfer Learning for MCI Conversion Prediction.
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DOI：
10.1109/tbme.2015.2404809
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发表时间：
2015-07
期刊：
影响因子：
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通讯作者：
Shen D
中科院分区：
文献类型：
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作者：
Cheng B;Liu M;Zhang D;Munsell BC;Shen D
机器学习方法越来越多地用于预测轻度认知障碍（MCI）向阿尔茨海默病（AD）的转换，通过将MCI转换者（MCI- c）从MCI非转换者（MCI- nc）进行分类。然而，大多数现有方法仅使用来自特定目标领域（例如MCI）的数据构建分类器，而忽略了其他相关领域（例如AD和正常控制（NC））的数据，这些数据可以提供有价值的信息来提高MCI转换预测的性能。为此，我们开发了一种新的用于MCI转换预测的领域迁移学习方法，该方法可以使用来自目标领域（即MCI）和辅助领域（即AD和NC）的数据。具体而言，所提出的方法由三个关键部分组成：1)从具有不同成像模式的目标域和辅助域中选择信息量最大的特征子集的域转移特征选择（DTFS）组件，2)从具有不同数据模式的相同目标域和辅助域中选择信息量最大的样本子集的域转移样本选择（DTSS）组件，3)融合所选特征和样本进行MCI-C和MCI-NC患者分离的域转移支持向量机（DTSVM）分类组件。我们用MRI、FDG-PET和CSF数据对来自阿尔茨海默病神经影像学倡议（ADNI）的202名受试者进行了评估。实验结果表明，利用从AD和NC中学习到的额外领域知识，该方法可以将MCI-C患者与MCI-NC患者进行分类，准确率为79.4%。
Machine learning methods have been increasingly used to predict the conversion of mild cognitive impairment (MCI) to Alzheimer's disease (AD), by classifying MCI converters (MCI-C) from MCI non-converters (MCI-NC). However, most of existing methods construct classifiers using only data from one particular target domain (e.g., MCI), and ignore data in the other related domains (e.g., AD and normal control (NC)) that could provide valuable information to promote the performance of MCI conversion prediction. To this end, we develop a novel domain transfer learning method for MCI conversion prediction, which can use data from both the target domain (i.e., MCI) and the auxiliary domains (i.e., AD and NC). Specifically, the proposed method consists of three key components: 1) a domain transfer feature selection (DTFS) component that selects the most informative feature-subset from both target domain and auxiliary domains with different imaging modalities, 2) a domain transfer sample selection (DTSS) component that selects the most informative sample-subset from the same target and auxiliary domains with different data modalities, and 3) a domain transfer support vector machine (DTSVM) classification component that fuses the selected features and samples to separate MCI-C and MCI-NC patients. We evaluate our method on 202 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) with MRI, FDG-PET and CSF data. The experimental results show that the proposed method can classify MCI-C patients from MCI-NC patients with an accuracy of 79.4%, with the aid of additional domain knowledge learned from AD and NC.