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Proceedings Paper

Semi-supervised sparse representation classifier with random sample subset ensembles in fMRI-based brain state decoding
Author(s): Jing Zhang; Chuncheng Zhang; Sutao Song; Li Yao; Xiaojie Zhao; Zhiying Long
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Paper Abstract

Multivariate classification techniques have been widely applied to decode brain states using functional magnetic resonance imaging (fMRI). Because the number of labeled samples is limited by the financial and safety consideration during fMRI data acquirement, it is not easy to train a robust classifier for fMRI data. Recently, semi-supervised learning has been proposed to train the classifier using both labeled training data and unlabeled data. Moreover, sparse representation based classification (SRC) has seldom been applied to fMRI data, although it exhibits a state-of-the-art classification performance in image processing. In this study, we proposed semi-supervised SRC with random sample subset ensemble strategy (semiSRC-RSSE) that used the average of class-specific coefficients as the SRC classification criterion and dynamically update the training dataset using the random sample subset ensemble method to measure the confidence of the prediction of each test sample. The results of the simulated and real fMRI experiments showed that semiSRC-RSSE method largely improved the classification accuracy of SRC and had better performance than support vector machine (SVM) and semi-supervised SVM with the random sample subset ensemble strategy (semiSVM-RSSE).

Paper Details

Date Published: 12 March 2018
PDF: 7 pages
Proc. SPIE 10578, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging, 105782A (12 March 2018); doi: 10.1117/12.2292542
Show Author Affiliations
Jing Zhang, Beijing Normal Univ. (China)
Chuncheng Zhang, Beijing Normal Univ. (China)
Sutao Song, Univ. of Jinan (China)
Li Yao, Beijing Normal Univ. (China)
Xiaojie Zhao, Beijing Normal Univ. (China)
Zhiying Long, Beijing Normal Univ. (China)

Published in SPIE Proceedings Vol. 10578:
Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging
Barjor Gimi; Andrzej Krol, Editor(s)

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