Abstract
In conventional multi-modality based classification framework, feature selection is typically performed separately for each individual modality, ignoring potential strong inter-modality relationship of the same subject. To extract this inter-modality relationship, L2,1 norm-based multi-task learning approach can be used to jointly select common features from different modalities. Unfortunately, this approach overlooks different yet complementary information conveyed by different modalities. To address this issue, we propose a novel multi-task feature selection method to effectively preserve the complementary information between different modalities, improving brain disease classification accuracy. Specifically, a new constraint is introduced to preserve the inter-modality relationship by treating the feature selection procedure of each modality as a task. This constraint preserves distance between feature vectors from different modalities after projection to low dimensional feature space. We evaluated our method on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and obtained significant improvement on Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) classification compared to state-of-the-art methods.
| Original language | English |
|---|---|
| Title of host publication | Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention |
| Pages | 308-315 |
| Number of pages | 8 |
| Volume | 16 |
| Edition | Pt 1 |
| Publication status | Published - 2013 Dec 1 |
| Externally published | Yes |
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ASJC Scopus subject areas
- Medicine(all)
Cite this
Inter-modality relationship constrained multi-task feature selection for AD/MCI classification. / Liu, Feng; Wee, Chong Yaw; Chen, Huafu; Shen, Dinggang.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 16 Pt 1. ed. 2013. p. 308-315.Research output: Chapter in Book/Report/Conference proceeding › Chapter
}
TY - CHAP
T1 - Inter-modality relationship constrained multi-task feature selection for AD/MCI classification.
AU - Liu, Feng
AU - Wee, Chong Yaw
AU - Chen, Huafu
AU - Shen, Dinggang
PY - 2013/12/1
Y1 - 2013/12/1
N2 - In conventional multi-modality based classification framework, feature selection is typically performed separately for each individual modality, ignoring potential strong inter-modality relationship of the same subject. To extract this inter-modality relationship, L2,1 norm-based multi-task learning approach can be used to jointly select common features from different modalities. Unfortunately, this approach overlooks different yet complementary information conveyed by different modalities. To address this issue, we propose a novel multi-task feature selection method to effectively preserve the complementary information between different modalities, improving brain disease classification accuracy. Specifically, a new constraint is introduced to preserve the inter-modality relationship by treating the feature selection procedure of each modality as a task. This constraint preserves distance between feature vectors from different modalities after projection to low dimensional feature space. We evaluated our method on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and obtained significant improvement on Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) classification compared to state-of-the-art methods.
AB - In conventional multi-modality based classification framework, feature selection is typically performed separately for each individual modality, ignoring potential strong inter-modality relationship of the same subject. To extract this inter-modality relationship, L2,1 norm-based multi-task learning approach can be used to jointly select common features from different modalities. Unfortunately, this approach overlooks different yet complementary information conveyed by different modalities. To address this issue, we propose a novel multi-task feature selection method to effectively preserve the complementary information between different modalities, improving brain disease classification accuracy. Specifically, a new constraint is introduced to preserve the inter-modality relationship by treating the feature selection procedure of each modality as a task. This constraint preserves distance between feature vectors from different modalities after projection to low dimensional feature space. We evaluated our method on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and obtained significant improvement on Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) classification compared to state-of-the-art methods.
UR - http://www.scopus.com/inward/record.url?scp=84894638738&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84894638738&partnerID=8YFLogxK
M3 - Chapter
C2 - 24505680
AN - SCOPUS:84894638738
VL - 16
SP - 308
EP - 315
BT - Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
ER -