Cheng Bo, Liu Mingxia, Zhang Daoqiang, Munsell Brent C, Shen Dinggang
Nanjing University of Aeronautics and Astronautics.
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
IEEE Trans Biomed Eng. 2015 Jul;62(7):1805-1817. doi: 10.1109/TBME.2015.2404809. Epub 2015 Mar 2.
Machine learning methods have successfully been used to predict the conversion of mild cognitive impairment (MCI) to Alzheimer's disease (AD), by classifying MCI converters (MCI-C) from MCI nonconverters (MCI-NC). However, most existing methods construct classifiers using data from one particular target domain (e.g., MCI), and ignore data in other related domains (e.g., AD and normal control (NC)) that may provide valuable information to improve MCI conversion prediction performance. To address is limitation, 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 auxiliary domains (i.e., AD and NC). Specifically, the proposed method consists of three key components: 1) a domain transfer feature selection component that selects the most informative feature-subset from both target domain and auxiliary domains from different imaging modalities; 2) a domain transfer sample selection component that selects the most informative sample-subset from the same target and auxiliary domains from different data modalities; and 3) a domain transfer support vector machine 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) that have MRI, FDG-PET, and CSF data. The experimental results show 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.
机器学习方法已成功用于预测轻度认知障碍（MCI）向阿尔茨海默病（AD）的转化，通过将MCI转化者（MCI-C）与MCI非转化者（MCI-NC）进行分类。然而，大多数现有方法使用来自一个特定目标领域（如MCI）的数据构建分类器，而忽略了其他相关领域（如AD和正常对照（NC））中可能提供有价值信息以提高MCI转化预测性能的数据。为了解决这一局限性，我们开发了一种用于MCI转化预测的新型域转移学习方法，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组件组成：1）一个域转移特征选择组件，从不同成像模态的目标领域和辅助领域中选择信息量最大的特征子集；2）一个域转移样本选择组件，从不同数据模态的相同目标和辅助领域中选择信息量最大的样本子集；3）一个域转移支持向量机分类组件，融合所选特征和样本以区分MCI-C和MCI-NC患者。我们在来自阿尔茨海默病神经成像倡议（ADNI）的202名具有MRI、FDG-PET和脑脊液数据的受试者上评估了我们的方法。实验结果表明，借助从AD和NC中学到的额外领域知识，所提出的方法能够以79.4%的准确率将MCI-C患者与MCI-NC患者区分开来。
