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Published in:
2015 | OriginalPaper | Chapter
Discriminative Feature Selection for Multiple Ocular Diseases Classification by Sparse Induced Graph Regularized Group Lasso
Authors : Xiangyu Chen, Yanwu Xu, Shuicheng Yan, Tat-Seng Chua, Damon Wing Kee Wong, Tien Yin Wong, Jiang Liu
Published in: Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015
Publisher: Springer International Publishing
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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.