Joint feature-sample selection and robust classification for Parkinson’s disease diagnosis

Ehsan Adeli-Mosabbeb; Chong Yaw Wee; Le An; Feng Shi; Dinggang Shen

doi:10.1007/978-3-319-42016-5_12

Joint feature-sample selection and robust classification for Parkinson’s disease diagnosis

Ehsan Adeli-Mosabbeb, Chong Yaw Wee, Le An, Feng Shi, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

Abstract

Parkinson’s disease (PD) is an overwhelming neurodegenerative disorder caused by deterioration of a neurotransmitter, known as dopamine. Lack of this chemical messenger in the brain impairs several brain regions and yields to various movement and non-motor symptoms. The incidence of PD is considered to be doubled in the next two decades and this urges more researches on its early diagnosis and treatment. In this paper, we propose an approach to diagnose PD using magnetic resonance imaging (MRI) data. We first introduce a joint feature-sample selection method to select the optimal subset of samples and features for a reliable training process. This procedure selects the most discriminative features and discards poor sample (outliers). Then, a robust classification framework is proposed that can simultaneously de-noise the selected subset of features and samples, and learn a classification model. Our model can further de-noise the test samples based on the cleaned training data. Experimental results on both synthetic and a publicly available PD dataset show promising results.

Original language	English
Title of host publication	Medical Computer Vision
Subtitle of host publication	Algorithms for Big Data - International Workshop, MCV 2015 and Held in Conjunction with MICCAI 2015, Revised Selected Papers
Editors	Michael Kelm, Henning Müller, Bjoern Menze, Shaoting Zhang, Dimitris Metaxas, Georg Langs, Albert Montillo, Weidong Cai
Publisher	Springer Verlag
Pages	127-136
Number of pages	10
ISBN (Print)	9783319420158
DOIs	https://doi.org/10.1007/978-3-319-42016-5_12
Publication status	Published - 2016
Event	International Workshop on Medical Image Computing for Computer Assisted Intervention, 2015 MICCAI - Germany, Germany Duration: 2015 Oct 9 → 2015 Oct 9

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9601 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	International Workshop on Medical Image Computing for Computer Assisted Intervention, 2015 MICCAI
Country/Territory	Germany
City	Germany
Period	15/10/9 → 15/10/9

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2016.

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-42016-5_12

Cite this

Adeli-Mosabbeb, E., Wee, C. Y., An, L., Shi, F., & Shen, D. (2016). Joint feature-sample selection and robust classification for Parkinson’s disease diagnosis. In M. Kelm, H. Müller, B. Menze, S. Zhang, D. Metaxas, G. Langs, A. Montillo, & W. Cai (Eds.), Medical Computer Vision: Algorithms for Big Data - International Workshop, MCV 2015 and Held in Conjunction with MICCAI 2015, Revised Selected Papers (pp. 127-136). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9601 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-42016-5_12

Joint feature-sample selection and robust classification for Parkinson’s disease diagnosis. / Adeli-Mosabbeb, Ehsan; Wee, Chong Yaw; An, Le et al.
Medical Computer Vision: Algorithms for Big Data - International Workshop, MCV 2015 and Held in Conjunction with MICCAI 2015, Revised Selected Papers. ed. / Michael Kelm; Henning Müller; Bjoern Menze; Shaoting Zhang; Dimitris Metaxas; Georg Langs; Albert Montillo; Weidong Cai. Springer Verlag, 2016. p. 127-136 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9601 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Adeli-Mosabbeb, E, Wee, CY, An, L, Shi, F & Shen, D 2016, Joint feature-sample selection and robust classification for Parkinson’s disease diagnosis. in M Kelm, H Müller, B Menze, S Zhang, D Metaxas, G Langs, A Montillo & W Cai (eds), Medical Computer Vision: Algorithms for Big Data - International Workshop, MCV 2015 and Held in Conjunction with MICCAI 2015, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9601 LNCS, Springer Verlag, pp. 127-136, International Workshop on Medical Image Computing for Computer Assisted Intervention, 2015 MICCAI, Germany, Germany, 15/10/9. https://doi.org/10.1007/978-3-319-42016-5_12

Adeli-Mosabbeb E, Wee CY, An L, Shi F, Shen D. Joint feature-sample selection and robust classification for Parkinson’s disease diagnosis. In Kelm M, Müller H, Menze B, Zhang S, Metaxas D, Langs G, Montillo A, Cai W, editors, Medical Computer Vision: Algorithms for Big Data - International Workshop, MCV 2015 and Held in Conjunction with MICCAI 2015, Revised Selected Papers. Springer Verlag. 2016. p. 127-136. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-42016-5_12

Adeli-Mosabbeb, Ehsan ; Wee, Chong Yaw ; An, Le et al. / Joint feature-sample selection and robust classification for Parkinson’s disease diagnosis. Medical Computer Vision: Algorithms for Big Data - International Workshop, MCV 2015 and Held in Conjunction with MICCAI 2015, Revised Selected Papers. editor / Michael Kelm ; Henning Müller ; Bjoern Menze ; Shaoting Zhang ; Dimitris Metaxas ; Georg Langs ; Albert Montillo ; Weidong Cai. Springer Verlag, 2016. pp. 127-136 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Joint feature-sample selection and robust classification for Parkinson’s disease diagnosis

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