Abstract
The symptoms of neuropsychiatric systemic lupus erythematosus (NPSLE) are subtle and elusive at the early stages. \(^1\)H-MRS (proton magnetic resonance spectrum) imaging technology can detect more detailed early appearances of NPSLE compared with conventional ones. However, the noises in \(^1\)H-MRS data often bring bias in the diagnostic process. Moreover, the features of specific brain regions are positively correlated with a certain category but may be redundant for other categories. To overcome these issues, we propose a robust exclusive adaptive sparse feature selection (REASFS) algorithm for early diagnosis and biomarker discovery of NPSLE. Specifically, we employ generalized correntropic loss to address non-Gaussian noise and outliers. Then, we develop a generalized correntropy-induced exclusive \(\ell _{2,1}\) regularization to adaptively accommodate various sparsity levels and preserve informative features. We conduct sufficient experiments on a benchmark NPSLE dataset, and the experimental results demonstrate the superiority of our proposed method compared with state-of-the-art ones.
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Acknowledgements
This work was supported by a grant of the Innovation and Technology Fund - Guangdong-Hong Kong Technology Cooperation Funding Scheme (No. GHP/051/20GD), the Project of Strategic Importance in The Hong Kong Polytechnic University (No. 1-ZE2Q), the 2022 Guangdong Basic and Applied Basic Research Foundation (No. 2022A1515011590), the National Natural Science Foundation of China (No. 61902232), and the 2020 Li Ka Shing Foundation Cross-Disciplinary Research Grant (No. 2020LKSFG05D).
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Quan, T., Yuan, Y., Luo, Y., Zhou, T., Qin, J. (2023). Robust Exclusive Adaptive Sparse Feature Selection for Biomarker Discovery and Early Diagnosis of Neuropsychiatric Systemic Lupus Erythematosus. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14224. Springer, Cham. https://doi.org/10.1007/978-3-031-43904-9_13
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