Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/137182
Title: Parkinson's disease diagnosis via joint learning from multiple modalities and relations
Authors: Lei, Haijun
Huang, Zhongwei
Zhou, Feng
Elazab, Ahmed
Tan, Ee-Leng
Li, Hancong
Qin, Jing
Lei, Baiying
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: Lei, H., Huang, Z., Zhou, F., Elazab, A., Tan, E.-L., Li, H., . . .Lei, B. (2019). Parkinson's disease diagnosis via joint learning from multiple modalities and relations. IEEE Journal of Biomedical and Health Informatics, 23(4), 1437-1449. doi:10.1109/JBHI.2018.2868420
Journal: IEEE journal of biomedical and health informatics
Abstract: Parkinson's disease (PD) is a neurodegenerative progressive disease that mainly affects the motor systems of patients. To slow this disease deterioration, early and accurate diagnosis of PD is an effective way, which alleviates mental and physical sufferings by clinical intervention. In this paper, we propose a joint regression and classification framework for PD diagnosis via magnetic resonance and diffusion tensor imaging data. Specifically, we devise a unified multitask feature selection model to explore multiple relationships among features, samples, and clinical scores. We regress four clinical variables of depression, sleep, olfaction, cognition scores, as well as perform the classification of PD disease from the multimodal data. The multitask model explores the relationships at the level of clinical scores, image features, and subjects, to select the most informative and diseased-related features for diagnosis. The proposed method is evaluated on the public Parkinson's progression markers initiative dataset. The extensive experimental results show that the multitask framework can effectively boost the performance of regression and classification and outperforms other state-of-the-art methods. The computerized predictions of clinical scores and label for PD diagnosis may offer quantitative reference for decision support as well.
URI: https://hdl.handle.net/10356/137182
ISSN: 2168-2194
DOI: 10.1109/JBHI.2018.2868420
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/JBHI.2018.2868420.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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