Progressive graph-based transductive learning for multi-modal classification of brain disorder disease

Zhengxia Wang, Xiaofeng Zhu, Ehsan Adeli, Yingying Zhu, Chen Zu, Feiping Nie, Dinggang Shen, Guorong Wu

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

3 Citations (Scopus)

Abstract

Graph-based Transductive Learning (GTL) is a powerful tool in computer-assisted diagnosis,especially when the training data is not sufficient to build reliable classifiers. Conventional GTL approaches first construct a fixed subject-wise graph based on the similarities of observed features (i.e.,extracted from imaging data) in the feature domain,and then follow the established graph to propagate the existing labels from training to testing data in the label domain. However,such a graph is exclusively learned in the feature domain and may not be necessarily optimal in the label domain. This may eventually undermine the classification accuracy. To address this issue,we propose a progressive GTL (pGTL) method to progressively find an intrinsic data representation. To achieve this,our pGTL method iteratively (1) refines the subject-wise relationships observed in the feature domain using the learned intrinsic data representation in the label domain,(2) updates the intrinsic data representation from the refined subject-wise relationships,and (3) verifies the intrinsic data representation on the training data,in order to guarantee an optimal classification on the new testing data. Furthermore,we extend our pGTL to incorporate multi-modal imaging data,to improve the classification accuracy and robustness as multi-modal imaging data can provide complementary information. Promising classification results in identifying Alzheimer’s disease (AD),Mild Cognitive Impairment (MCI),and Normal Control (NC) subjects are achieved using MRI and PET data.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
PublisherSpringer Verlag
Pages291-299
Number of pages9
Volume9900 LNCS
ISBN (Print)9783319467191
DOIs
Publication statusPublished - 2016
Externally publishedYes
Event1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 - Athens, Greece
Duration: 2016 Oct 212016 Oct 21

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9900 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016
CountryGreece
CityAthens
Period16/10/2116/10/21

Fingerprint

Disorder
Labels
Brain
Graph in graph theory
Imaging techniques
Testing
Magnetic resonance imaging
Classifiers
Imaging
Learning
Alzheimer's Disease
Update
Classifier
Sufficient
Verify
Robustness

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Wang, Z., Zhu, X., Adeli, E., Zhu, Y., Zu, C., Nie, F., ... Wu, G. (2016). Progressive graph-based transductive learning for multi-modal classification of brain disorder disease. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings (Vol. 9900 LNCS, pp. 291-299). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9900 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46720-7_34

Progressive graph-based transductive learning for multi-modal classification of brain disorder disease. / Wang, Zhengxia; Zhu, Xiaofeng; Adeli, Ehsan; Zhu, Yingying; Zu, Chen; Nie, Feiping; Shen, Dinggang; Wu, Guorong.

Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Vol. 9900 LNCS Springer Verlag, 2016. p. 291-299 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9900 LNCS).

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

Wang, Z, Zhu, X, Adeli, E, Zhu, Y, Zu, C, Nie, F, Shen, D & Wu, G 2016, Progressive graph-based transductive learning for multi-modal classification of brain disorder disease. in Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. vol. 9900 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9900 LNCS, Springer Verlag, pp. 291-299, 1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, Athens, Greece, 16/10/21. https://doi.org/10.1007/978-3-319-46720-7_34
Wang Z, Zhu X, Adeli E, Zhu Y, Zu C, Nie F et al. Progressive graph-based transductive learning for multi-modal classification of brain disorder disease. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Vol. 9900 LNCS. Springer Verlag. 2016. p. 291-299. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-46720-7_34
Wang, Zhengxia ; Zhu, Xiaofeng ; Adeli, Ehsan ; Zhu, Yingying ; Zu, Chen ; Nie, Feiping ; Shen, Dinggang ; Wu, Guorong. / Progressive graph-based transductive learning for multi-modal classification of brain disorder disease. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Vol. 9900 LNCS Springer Verlag, 2016. pp. 291-299 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{79740b6736364dd69e3a192cf3ef4cb0,
title = "Progressive graph-based transductive learning for multi-modal classification of brain disorder disease",
abstract = "Graph-based Transductive Learning (GTL) is a powerful tool in computer-assisted diagnosis,especially when the training data is not sufficient to build reliable classifiers. Conventional GTL approaches first construct a fixed subject-wise graph based on the similarities of observed features (i.e.,extracted from imaging data) in the feature domain,and then follow the established graph to propagate the existing labels from training to testing data in the label domain. However,such a graph is exclusively learned in the feature domain and may not be necessarily optimal in the label domain. This may eventually undermine the classification accuracy. To address this issue,we propose a progressive GTL (pGTL) method to progressively find an intrinsic data representation. To achieve this,our pGTL method iteratively (1) refines the subject-wise relationships observed in the feature domain using the learned intrinsic data representation in the label domain,(2) updates the intrinsic data representation from the refined subject-wise relationships,and (3) verifies the intrinsic data representation on the training data,in order to guarantee an optimal classification on the new testing data. Furthermore,we extend our pGTL to incorporate multi-modal imaging data,to improve the classification accuracy and robustness as multi-modal imaging data can provide complementary information. Promising classification results in identifying Alzheimer’s disease (AD),Mild Cognitive Impairment (MCI),and Normal Control (NC) subjects are achieved using MRI and PET data.",
author = "Zhengxia Wang and Xiaofeng Zhu and Ehsan Adeli and Yingying Zhu and Chen Zu and Feiping Nie and Dinggang Shen and Guorong Wu",
year = "2016",
doi = "10.1007/978-3-319-46720-7_34",
language = "English",
isbn = "9783319467191",
volume = "9900 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "291--299",
booktitle = "Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings",

}

TY - GEN

T1 - Progressive graph-based transductive learning for multi-modal classification of brain disorder disease

AU - Wang, Zhengxia

AU - Zhu, Xiaofeng

AU - Adeli, Ehsan

AU - Zhu, Yingying

AU - Zu, Chen

AU - Nie, Feiping

AU - Shen, Dinggang

AU - Wu, Guorong

PY - 2016

Y1 - 2016

N2 - Graph-based Transductive Learning (GTL) is a powerful tool in computer-assisted diagnosis,especially when the training data is not sufficient to build reliable classifiers. Conventional GTL approaches first construct a fixed subject-wise graph based on the similarities of observed features (i.e.,extracted from imaging data) in the feature domain,and then follow the established graph to propagate the existing labels from training to testing data in the label domain. However,such a graph is exclusively learned in the feature domain and may not be necessarily optimal in the label domain. This may eventually undermine the classification accuracy. To address this issue,we propose a progressive GTL (pGTL) method to progressively find an intrinsic data representation. To achieve this,our pGTL method iteratively (1) refines the subject-wise relationships observed in the feature domain using the learned intrinsic data representation in the label domain,(2) updates the intrinsic data representation from the refined subject-wise relationships,and (3) verifies the intrinsic data representation on the training data,in order to guarantee an optimal classification on the new testing data. Furthermore,we extend our pGTL to incorporate multi-modal imaging data,to improve the classification accuracy and robustness as multi-modal imaging data can provide complementary information. Promising classification results in identifying Alzheimer’s disease (AD),Mild Cognitive Impairment (MCI),and Normal Control (NC) subjects are achieved using MRI and PET data.

AB - Graph-based Transductive Learning (GTL) is a powerful tool in computer-assisted diagnosis,especially when the training data is not sufficient to build reliable classifiers. Conventional GTL approaches first construct a fixed subject-wise graph based on the similarities of observed features (i.e.,extracted from imaging data) in the feature domain,and then follow the established graph to propagate the existing labels from training to testing data in the label domain. However,such a graph is exclusively learned in the feature domain and may not be necessarily optimal in the label domain. This may eventually undermine the classification accuracy. To address this issue,we propose a progressive GTL (pGTL) method to progressively find an intrinsic data representation. To achieve this,our pGTL method iteratively (1) refines the subject-wise relationships observed in the feature domain using the learned intrinsic data representation in the label domain,(2) updates the intrinsic data representation from the refined subject-wise relationships,and (3) verifies the intrinsic data representation on the training data,in order to guarantee an optimal classification on the new testing data. Furthermore,we extend our pGTL to incorporate multi-modal imaging data,to improve the classification accuracy and robustness as multi-modal imaging data can provide complementary information. Promising classification results in identifying Alzheimer’s disease (AD),Mild Cognitive Impairment (MCI),and Normal Control (NC) subjects are achieved using MRI and PET data.

UR - http://www.scopus.com/inward/record.url?scp=84996598688&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84996598688&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-46720-7_34

DO - 10.1007/978-3-319-46720-7_34

M3 - Conference contribution

SN - 9783319467191

VL - 9900 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 291

EP - 299

BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings

PB - Springer Verlag

ER -