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 proceedingConference 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 languageEnglish
Title of host publicationMedical Computer Vision: Algorithms for Big Data - International Workshop, MCV 2015 and Held in Conjunction with MICCAI 2015, Revised Selected Papers
PublisherSpringer Verlag
Pages127-136
Number of pages10
Volume9601
ISBN (Print)9783319420158
DOIs
Publication statusPublished - 2016
Externally publishedYes
EventInternational Workshop on Medical Image Computing for Computer Assisted Intervention, 2015 MICCAI - Germany, Germany
Duration: 2015 Oct 92015 Oct 9

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9601
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

OtherInternational Workshop on Medical Image Computing for Computer Assisted Intervention, 2015 MICCAI
CountryGermany
CityGermany
Period15/10/915/10/9

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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 Medical Computer Vision: Algorithms for Big Data - International Workshop, MCV 2015 and Held in Conjunction with MICCAI 2015, Revised Selected Papers (Vol. 9601, pp. 127-136). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9601). Springer Verlag. https://doi.org/10.1007/978-3-319-42016-5_12