Abstract
As the early stage of Alzheimer’s disease (AD), mild cognitive impairment (MCI) has high chance to convert to AD. Effective prediction of such conversion from MCI to AD is of great importance for early diagnosis of AD and also for evaluating AD risk pre-symptomatically. Unlike most previous methods that used only the samples from a target domain to train a classifier, in this paper, we propose a novel multimodal manifold-regularized transfer learning (M2TL) method that jointly utilizes samples from another domain (e.g., AD vs. normal controls (NC)) as well as unlabeled samples to boost the performance of the MCI conversion prediction. Specifically, the proposed M2TL method includes two key components. The first one is a kernel-based maximum mean discrepancy criterion, which helps eliminate the potential negative effect induced by the distributional difference between the auxiliary domain (i.e., AD and NC) and the target domain (i.e., MCI converters (MCI-C) and MCI non-converters (MCI-NC)). The second one is a semi-supervised multimodal manifold-regularized least squares classification method, where the target-domain samples, the auxiliary-domain samples, and the unlabeled samples can be jointly used for training our classifier. Furthermore, with the integration of a group sparsity constraint into our objective function, the proposed M2TL has a capability of selecting the informative samples to build a robust classifier. Experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database validate the effectiveness of the proposed method by significantly improving the classification accuracy of 80.1 % for MCI conversion prediction, and also outperforming the state-of-the-art methods.
| Original language | English |
|---|---|
| Pages (from-to) | 913-926 |
| Number of pages | 14 |
| Journal | Brain Imaging and Behavior |
| Volume | 9 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 2015 Dec 1 |
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Keywords
- Manifold regularization
- Mild cognitive impairment conversion
- Multimodal classification
- Sample selection
- Semi-supervised learning
- Transfer learning
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging
- Behavioral Neuroscience
- Cellular and Molecular Neuroscience
- Cognitive Neuroscience
- Neurology
- Psychiatry and Mental health
- Clinical Neurology
Cite this
Multimodal manifold-regularized transfer learning for MCI conversion prediction. / Cheng, Bo; Liu, Mingxia; Suk, Heung-Il; Shen, Dinggang; Zhang, Daoqiang.
In: Brain Imaging and Behavior, Vol. 9, No. 4, 01.12.2015, p. 913-926.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Multimodal manifold-regularized transfer learning for MCI conversion prediction
AU - Cheng, Bo
AU - Liu, Mingxia
AU - Suk, Heung-Il
AU - Shen, Dinggang
AU - Zhang, Daoqiang
PY - 2015/12/1
Y1 - 2015/12/1
N2 - As the early stage of Alzheimer’s disease (AD), mild cognitive impairment (MCI) has high chance to convert to AD. Effective prediction of such conversion from MCI to AD is of great importance for early diagnosis of AD and also for evaluating AD risk pre-symptomatically. Unlike most previous methods that used only the samples from a target domain to train a classifier, in this paper, we propose a novel multimodal manifold-regularized transfer learning (M2TL) method that jointly utilizes samples from another domain (e.g., AD vs. normal controls (NC)) as well as unlabeled samples to boost the performance of the MCI conversion prediction. Specifically, the proposed M2TL method includes two key components. The first one is a kernel-based maximum mean discrepancy criterion, which helps eliminate the potential negative effect induced by the distributional difference between the auxiliary domain (i.e., AD and NC) and the target domain (i.e., MCI converters (MCI-C) and MCI non-converters (MCI-NC)). The second one is a semi-supervised multimodal manifold-regularized least squares classification method, where the target-domain samples, the auxiliary-domain samples, and the unlabeled samples can be jointly used for training our classifier. Furthermore, with the integration of a group sparsity constraint into our objective function, the proposed M2TL has a capability of selecting the informative samples to build a robust classifier. Experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database validate the effectiveness of the proposed method by significantly improving the classification accuracy of 80.1 % for MCI conversion prediction, and also outperforming the state-of-the-art methods.
AB - As the early stage of Alzheimer’s disease (AD), mild cognitive impairment (MCI) has high chance to convert to AD. Effective prediction of such conversion from MCI to AD is of great importance for early diagnosis of AD and also for evaluating AD risk pre-symptomatically. Unlike most previous methods that used only the samples from a target domain to train a classifier, in this paper, we propose a novel multimodal manifold-regularized transfer learning (M2TL) method that jointly utilizes samples from another domain (e.g., AD vs. normal controls (NC)) as well as unlabeled samples to boost the performance of the MCI conversion prediction. Specifically, the proposed M2TL method includes two key components. The first one is a kernel-based maximum mean discrepancy criterion, which helps eliminate the potential negative effect induced by the distributional difference between the auxiliary domain (i.e., AD and NC) and the target domain (i.e., MCI converters (MCI-C) and MCI non-converters (MCI-NC)). The second one is a semi-supervised multimodal manifold-regularized least squares classification method, where the target-domain samples, the auxiliary-domain samples, and the unlabeled samples can be jointly used for training our classifier. Furthermore, with the integration of a group sparsity constraint into our objective function, the proposed M2TL has a capability of selecting the informative samples to build a robust classifier. Experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database validate the effectiveness of the proposed method by significantly improving the classification accuracy of 80.1 % for MCI conversion prediction, and also outperforming the state-of-the-art methods.
KW - Manifold regularization
KW - Mild cognitive impairment conversion
KW - Multimodal classification
KW - Sample selection
KW - Semi-supervised learning
KW - Transfer learning
UR - http://www.scopus.com/inward/record.url?scp=84948567283&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84948567283&partnerID=8YFLogxK
U2 - 10.1007/s11682-015-9356-x
DO - 10.1007/s11682-015-9356-x
M3 - Article
C2 - 25702248
AN - SCOPUS:84948567283
VL - 9
SP - 913
EP - 926
JO - Brain Imaging and Behavior
JF - Brain Imaging and Behavior
SN - 1931-7557
IS - 4
ER -