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Discriminative feature selection for multiple ocular diseases classification by sparse induced graph regularized group lasso

Publication ,  Conference
Chen, X; Xu, Y; Yan, S; Chua, TS; Wong, DWK; Wong, TY; Liu, J
Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
January 1, 2015

Glaucoma, Pathological Myopia (PM), and Age-related Macular Degeneration (AMD) are three leading ocular diseases worldwide. Visual features extracted from retinal fundus images have been increasingly used for detecting these three diseases. In this paper, we present a discriminative feature selection model based on multi-task learning, which imposes the exclusive group lasso regularization for competitive sparse feature selection and the graph Laplacian regularization to embed the correlations among multiple diseases. Moreover, this multi-task linear discriminative model is able to simultaneously select sparse features and detect multiple ocular diseases. Extensive experiments are conducted to validate the proposed framework on the SiMES dataset. From the Area Under Curve (AUC) results in multiple ocular diseases classification, our method is shown to outperform the state-of-the-art algorithms.

Duke Scholars

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

ISBN

9783319245706

Publication Date

January 1, 2015

Volume

9350

Start / End Page

11 / 19

Related Subject Headings

  • Artificial Intelligence & Image Processing
 

Citation

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MLA
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Chen, X., Xu, Y., Yan, S., Chua, T. S., Wong, D. W. K., Wong, T. Y., & Liu, J. (2015). Discriminative feature selection for multiple ocular diseases classification by sparse induced graph regularized group lasso. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9350, pp. 11–19). https://doi.org/10.1007/978-3-319-24571-3_2
Chen, X., Y. Xu, S. Yan, T. S. Chua, D. W. K. Wong, T. Y. Wong, and J. Liu. “Discriminative feature selection for multiple ocular diseases classification by sparse induced graph regularized group lasso.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9350:11–19, 2015. https://doi.org/10.1007/978-3-319-24571-3_2.
Chen X, Xu Y, Yan S, Chua TS, Wong DWK, Wong TY, et al. Discriminative feature selection for multiple ocular diseases classification by sparse induced graph regularized group lasso. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2015. p. 11–9.
Chen, X., et al. “Discriminative feature selection for multiple ocular diseases classification by sparse induced graph regularized group lasso.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9350, 2015, pp. 11–19. Scopus, doi:10.1007/978-3-319-24571-3_2.
Chen X, Xu Y, Yan S, Chua TS, Wong DWK, Wong TY, Liu J. Discriminative feature selection for multiple ocular diseases classification by sparse induced graph regularized group lasso. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2015. p. 11–19.
Journal cover image

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

ISBN

9783319245706

Publication Date

January 1, 2015

Volume

9350

Start / End Page

11 / 19

Related Subject Headings

  • Artificial Intelligence & Image Processing