Abstract
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.
| Original language | English |
|---|---|
| Article number | 10 |
| Pages (from-to) | 1805-1817 |
| Number of pages | 13 |
| Journal | IEEE Transactions on Biomedical Engineering |
| Volume | 62 |
| Issue number | 7 |
| DOIs | |
| Publication status | Published - 2015 Jul 1 |
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Keywords
- Alzheimer's Disease
- Domain Transfer Learning
- Feature Selection
- Mild Cognitive Impairment Converters
- Sample Selection
ASJC Scopus subject areas
- Biomedical Engineering
Cite this
Domain Transfer Learning for MCI Conversion Prediction. / Cheng, Bo; Liu, Mingxia; Zhang, Daoqiang; Munsell, Brent C.; Shen, Dinggang.
In: IEEE Transactions on Biomedical Engineering, Vol. 62, No. 7, 10, 01.07.2015, p. 1805-1817.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Domain Transfer Learning for MCI Conversion Prediction
AU - Cheng, Bo
AU - Liu, Mingxia
AU - Zhang, Daoqiang
AU - Munsell, Brent C.
AU - Shen, Dinggang
PY - 2015/7/1
Y1 - 2015/7/1
N2 - 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.
AB - 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.
KW - Alzheimer's Disease
KW - Domain Transfer Learning
KW - Feature Selection
KW - Mild Cognitive Impairment Converters
KW - Sample Selection
UR - http://www.scopus.com/inward/record.url?scp=84933053884&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84933053884&partnerID=8YFLogxK
U2 - 10.1109/TBME.2015.2404809
DO - 10.1109/TBME.2015.2404809
M3 - Article
C2 - 25751861
AN - SCOPUS:84933053884
VL - 62
SP - 1805
EP - 1817
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
SN - 0018-9294
IS - 7
M1 - 10
ER -