Multi-modal component subspace-similarity-based multi-kernel SVM for schizophrenia classification

Shuang Gao, Vince D. Calhoun, Jing Sui

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Heterogeneous multi-modal medical imaging data need to be properly handled in classification. Currently, generating models using multi-modal imaging data has become a common practice and greatly benefits the brain disorder diagnosis, which also holds considerable clinical potential. Although the majority of classification studies focus on using features from single modality, there is substantial evidence suggesting that classification based on multi-modal features is on upward trend. Hence, effective integration of heterogeneous data is in urgent demand. Here, we proposed a multi-kernel SVM for schizophrenia classification with nested 10-fold cross validation, which could integrate multi-modal data using the subspace similarity of the decomposed components in each MRI modality. To validate the effectiveness of the proposed method, we performed experiments on two independent datasets with three different modalities to classify schizophrenia patients and healthy controls. Specifically, multi-modal fusion method was first applied on preprocessed fMRI, DTI and sMRI data to generate components that could be used for classification. Then multi-kernel SVM models were trained on the selected component features using subspace similarity measures, and were tested on independent validation data across sites. The results on both datasets demonstrated that our method achieved accuracies of 87.6% and 79.9% separately on two datasets when combining all three modalities, which outperformed alternative methods and might provide potential biomarkers for cross-site classification and co-varying components among different modalities.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2020
Subtitle of host publicationComputer-Aided Diagnosis
EditorsHorst K. Hahn, Maciej A. Mazurowski
PublisherSPIE
ISBN (Electronic)9781510633957
DOIs
StatePublished - Jan 1 2020
Externally publishedYes
EventMedical Imaging 2020: Computer-Aided Diagnosis - Houston, United States
Duration: Feb 16 2020Feb 19 2020

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume11314
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2020: Computer-Aided Diagnosis
CountryUnited States
CityHouston
Period2/16/202/19/20

Keywords

  • mcca+jica
  • multi-kernel svm
  • multi-modal
  • schizophrenia
  • subspace similarity

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Gao, S., Calhoun, V. D., & Sui, J. (2020). Multi-modal component subspace-similarity-based multi-kernel SVM for schizophrenia classification. In H. K. Hahn, & M. A. Mazurowski (Eds.), Medical Imaging 2020: Computer-Aided Diagnosis [113143X] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 11314). SPIE. https://doi.org/10.1117/12.2550339