Domain transfer learning for MCI conversion prediction.
Domain transfer learning for MCI conversion prediction.
复制标题
用于 MCI 转换预测的域迁移学习
DOI：
10.1007/978-3-642-33415-3_11
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Shen, Dinggang
中科院分区：
文献类型：
--
作者：
Cheng, Bo;Zhang, Daoqiang;Shen, Dinggang
在阿尔茨海默病（AD）的研究中，鉴别轻度认知障碍（MCI）转换者（MCI- c）和非MCI转换者（MCI- nc）越来越受到关注。注意MCI是AD的前驱阶段，有可能转化为AD。大多数传统的MCI转换预测方法只学习MCI被试（包括MCI- c和MCI-NC）的信息，而不学习其他相关的被试，如AD和正常对照（NC），这实际上有助于MCI- c和MCI-NC之间的分类。本文提出了一种新的用于MCI转换预测的领域迁移学习方法。与大多数现有方法不同的是，我们利用在AD和NC科目中学习到的领域知识作为辅助领域对MCI-C和MCI-NC进行分类，进一步提高了分类性能。该方法包含两个关键部分：(1)辅助领域知识转移的跨域核学习，(2)跨域和辅助领域知识融合的自适应支持向量机（SVM）决策函数构建。在Alzheimer 's Disease Neuroimaging Initiative （ADNI）数据库上的实验结果表明，该方法可以借助从AD和NC受试者中学习到的领域知识，显著提高MCI-C和MCI-NC之间的分类性能。
In recent studies of Alzheimer’s disease (AD), it has increasing attentions in identifying mild cognitive impairment (MCI) converters (MCI-C) from MCI non-converters (MCI-NC). Note that MCI is a prodromal stage of AD, with possibility to convert to AD. Most traditional methods for MCI conversion prediction learn information only from MCI subjects (including MCI-C and MCI-NC), not from other related subjects, e.g., AD and normal controls (NC), which can actually aid the classification between MCI-C and MCI-NC. In this paper, we propose a novel domain-transfer learning method for MCI conversion prediction. Different from most existing methods, we classify MCI-C and MCI-NC with aid from the domain knowledge learned with AD and NC subjects as auxiliary domain to further improve the classification performance. Our method contains two key components: (1) the cross-domain kernel learning for transferring auxiliary domain knowledge, and (2) the adapted support vector machine (SVM) decision function construction for cross-domain and auxiliary domain knowledge fusion. Experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database show that the proposed method can significantly improve the classification performance between MCI-C and MCI-NC, with aid of domain knowledge learned from AD and NC subjects.
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影响因子：
3.7
作者：
Aksu Y;Miller DJ;Kesidis G;Bigler DC;Yang QX
通讯作者：
Yang QX
影响因子：
5.7
作者：
Cho Y;Seong JK;Jeong Y;Shin SY;Alzheimer's Disease Neuroimaging Initiative
通讯作者：
Alzheimer's Disease Neuroimaging Initiative
影响因子：
2.1
作者：
Risacher SL;Saykin AJ;West JD;Shen L;Firpi HA;McDonald BC;Alzheimer's Disease Neuroimaging Initiative (ADNI)
通讯作者：
Alzheimer's Disease Neuroimaging Initiative (ADNI)
影响因子：
10.6
作者：
Shen, DG;Davatzikos, C
通讯作者：
Davatzikos, C
影响因子：
5.7
作者：
Filipovych R;Davatzikos C;Alzheimer's Disease Neuroimaging Initiative
通讯作者：
Alzheimer's Disease Neuroimaging Initiative