Semi-supervised pattern classification

Application to structural MRI of Alzheimer's disease

Dong Hye Ye, Kilian M. Pohl, Christos Davatzikos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents an image-based classification method, and applies it to classification of brain MRI scans of individuals with Mild Cognitive Impairment (MCI). The high dimensionality of the image data is reduced using nonlinear manifold learning techniques, thereby yielding a low-dimensional embedding. Features of the embedding are used in conjunction with a semi-supervised classifier, which utilizes both labeled and unlabeled images to boost performance. The method is applied to 237 scans of MCI patients in order to predict conversion from MCI to Alzheimer's Disease. Experimental results demonstrate better prediction accuracy compared to a state-of-the-art method.

Original languageEnglish (US)
Title of host publicationProceedings - International Workshop on Pattern Recognition in NeuroImaging, PRNI 2011
Pages1-4
Number of pages4
DOIs
StatePublished - 2011
Externally publishedYes
EventInternational Workshop on Pattern Recognition in NeuroImaging, PRNI 2011 - Seoul, Korea, Republic of
Duration: May 16 2011May 18 2011

Other

OtherInternational Workshop on Pattern Recognition in NeuroImaging, PRNI 2011
CountryKorea, Republic of
CitySeoul
Period5/16/115/18/11

Fingerprint

Magnetic resonance imaging
Pattern recognition
Alzheimer Disease
Brain
Classifiers
Magnetic Resonance Imaging
Learning
Cognitive Dysfunction

Keywords

  • Alzheimer's disease
  • Early detection
  • Manifold learning
  • Mild cognitive impairment
  • Semi-supervised

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Cite this

Ye, D. H., Pohl, K. M., & Davatzikos, C. (2011). Semi-supervised pattern classification: Application to structural MRI of Alzheimer's disease. In Proceedings - International Workshop on Pattern Recognition in NeuroImaging, PRNI 2011 (pp. 1-4). [5961297] https://doi.org/10.1109/PRNI.2011.12

Semi-supervised pattern classification : Application to structural MRI of Alzheimer's disease. / Ye, Dong Hye; Pohl, Kilian M.; Davatzikos, Christos.

Proceedings - International Workshop on Pattern Recognition in NeuroImaging, PRNI 2011. 2011. p. 1-4 5961297.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Ye, DH, Pohl, KM & Davatzikos, C 2011, Semi-supervised pattern classification: Application to structural MRI of Alzheimer's disease. in Proceedings - International Workshop on Pattern Recognition in NeuroImaging, PRNI 2011., 5961297, pp. 1-4, International Workshop on Pattern Recognition in NeuroImaging, PRNI 2011, Seoul, Korea, Republic of, 5/16/11. https://doi.org/10.1109/PRNI.2011.12
Ye DH, Pohl KM, Davatzikos C. Semi-supervised pattern classification: Application to structural MRI of Alzheimer's disease. In Proceedings - International Workshop on Pattern Recognition in NeuroImaging, PRNI 2011. 2011. p. 1-4. 5961297 https://doi.org/10.1109/PRNI.2011.12
Ye, Dong Hye ; Pohl, Kilian M. ; Davatzikos, Christos. / Semi-supervised pattern classification : Application to structural MRI of Alzheimer's disease. Proceedings - International Workshop on Pattern Recognition in NeuroImaging, PRNI 2011. 2011. pp. 1-4
@inproceedings{8c529263048140a7bfb30a923d00d6e7,
title = "Semi-supervised pattern classification: Application to structural MRI of Alzheimer's disease",
abstract = "This paper presents an image-based classification method, and applies it to classification of brain MRI scans of individuals with Mild Cognitive Impairment (MCI). The high dimensionality of the image data is reduced using nonlinear manifold learning techniques, thereby yielding a low-dimensional embedding. Features of the embedding are used in conjunction with a semi-supervised classifier, which utilizes both labeled and unlabeled images to boost performance. The method is applied to 237 scans of MCI patients in order to predict conversion from MCI to Alzheimer's Disease. Experimental results demonstrate better prediction accuracy compared to a state-of-the-art method.",
keywords = "Alzheimer's disease, Early detection, Manifold learning, Mild cognitive impairment, Semi-supervised",
author = "Ye, {Dong Hye} and Pohl, {Kilian M.} and Christos Davatzikos",
year = "2011",
doi = "10.1109/PRNI.2011.12",
language = "English (US)",
isbn = "9780769543994",
pages = "1--4",
booktitle = "Proceedings - International Workshop on Pattern Recognition in NeuroImaging, PRNI 2011",

}

TY - GEN

T1 - Semi-supervised pattern classification

T2 - Application to structural MRI of Alzheimer's disease

AU - Ye, Dong Hye

AU - Pohl, Kilian M.

AU - Davatzikos, Christos

PY - 2011

Y1 - 2011

N2 - This paper presents an image-based classification method, and applies it to classification of brain MRI scans of individuals with Mild Cognitive Impairment (MCI). The high dimensionality of the image data is reduced using nonlinear manifold learning techniques, thereby yielding a low-dimensional embedding. Features of the embedding are used in conjunction with a semi-supervised classifier, which utilizes both labeled and unlabeled images to boost performance. The method is applied to 237 scans of MCI patients in order to predict conversion from MCI to Alzheimer's Disease. Experimental results demonstrate better prediction accuracy compared to a state-of-the-art method.

AB - This paper presents an image-based classification method, and applies it to classification of brain MRI scans of individuals with Mild Cognitive Impairment (MCI). The high dimensionality of the image data is reduced using nonlinear manifold learning techniques, thereby yielding a low-dimensional embedding. Features of the embedding are used in conjunction with a semi-supervised classifier, which utilizes both labeled and unlabeled images to boost performance. The method is applied to 237 scans of MCI patients in order to predict conversion from MCI to Alzheimer's Disease. Experimental results demonstrate better prediction accuracy compared to a state-of-the-art method.

KW - Alzheimer's disease

KW - Early detection

KW - Manifold learning

KW - Mild cognitive impairment

KW - Semi-supervised

UR - http://www.scopus.com/inward/record.url?scp=80051984095&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=80051984095&partnerID=8YFLogxK

U2 - 10.1109/PRNI.2011.12

DO - 10.1109/PRNI.2011.12

M3 - Conference contribution

SN - 9780769543994

SP - 1

EP - 4

BT - Proceedings - International Workshop on Pattern Recognition in NeuroImaging, PRNI 2011

ER -