Inter-modality relationship constrained multi-task feature selection for AD/MCI classification.

Feng Liu, Chong Yaw Wee, Huafu Chen, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingChapter

9 Citations (Scopus)

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 languageEnglish
Title of host publicationMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Pages308-315
Number of pages8
Volume16
EditionPt 1
Publication statusPublished - 2013 Dec 1
Externally publishedYes

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Alzheimer Disease
Brain Diseases
Neuroimaging
Learning
Cognitive Dysfunction

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Liu, F., Wee, C. Y., Chen, H., & Shen, D. (2013). Inter-modality relationship constrained multi-task feature selection for AD/MCI classification. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention (Pt 1 ed., Vol. 16, pp. 308-315)

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 proceedingChapter

Liu, F, Wee, CY, Chen, H & Shen, D 2013, Inter-modality relationship constrained multi-task feature selection for AD/MCI classification. in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 1 edn, vol. 16, pp. 308-315.
Liu F, Wee CY, Chen H, Shen D. Inter-modality relationship constrained multi-task feature selection for AD/MCI classification. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 1 ed. Vol. 16. 2013. p. 308-315
Liu, Feng ; Wee, Chong Yaw ; Chen, Huafu ; Shen, Dinggang. / Inter-modality relationship constrained multi-task feature selection for AD/MCI classification. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 16 Pt 1. ed. 2013. pp. 308-315
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