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 proceedingConference contribution

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 2013 - 16th International Conference, Proceedings
Pages308-315
Number of pages8
EditionPART 1
DOIs
Publication statusPublished - 2013
Event16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 - Nagoya, Japan
Duration: 2013 Sep 222013 Sep 26

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume8149 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
CountryJapan
CityNagoya
Period13/9/2213/9/26

Keywords

  • Alzheimer's Disease
  • Multi-kernel support vector machine
  • Multi-modality
  • Multi-task learning
  • Sparse representation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Inter-modality relationship constrained multi-task feature selection for AD/MCI classification'. Together they form a unique fingerprint.

  • 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 2013 - 16th International Conference, Proceedings (PART 1 ed., pp. 308-315). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8149 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-40811-3_39