Matrix-similarity based loss function and feature selection for Alzheimer's disease diagnosis

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

46 Citations (Scopus)

Abstract

Recent studies on Alzheimer's Disease (AD) or its prodromal stage, Mild Cognitive Impairment (MCI), diagnosis presented that the tasks of identifying brain disease status and predicting clinical scores based on neuroimaging features were highly related to each other. However, these tasks were often conducted independently in the previous studies. Regarding the feature selection, to our best knowledge, most of the previous work considered a loss function defined as an element-wise difference between the target values and the predicted ones. In this paper, we consider the problems of joint regression and classification for AD/MCI diagnosis and propose a novel matrix-similarity based loss function that uses high-level information inherent in the target response matrix and imposes the information to be preserved in the predicted response matrix. The newly devised loss function is combined with a group lasso method for joint feature selection across tasks, i.e., clinical scores prediction and disease status identification. We conducted experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and showed that the newly devised loss function was effective to enhance the performances of both clinical score prediction and disease status identification, outperforming the state-of-the-art methods.

Original languageEnglish
Title of host publicationProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
PublisherIEEE Computer Society
Pages3089-3096
Number of pages8
ISBN (Print)9781479951178, 9781479951178
DOIs
Publication statusPublished - 2014 Jan 1
Externally publishedYes
Event27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014 - Columbus, United States
Duration: 2014 Jun 232014 Jun 28

Other

Other27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014
CountryUnited States
CityColumbus
Period14/6/2314/6/28

Fingerprint

Feature extraction
Neuroimaging
Brain
Experiments

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

Cite this

Zhu, X., Suk, H-I., & Shen, D. (2014). Matrix-similarity based loss function and feature selection for Alzheimer's disease diagnosis. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 3089-3096). [6909791] IEEE Computer Society. https://doi.org/10.1109/CVPR.2014.395

Matrix-similarity based loss function and feature selection for Alzheimer's disease diagnosis. / Zhu, Xiaofeng; Suk, Heung-Il; Shen, Dinggang.

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 2014. p. 3089-3096 6909791.

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

Zhu, X, Suk, H-I & Shen, D 2014, Matrix-similarity based loss function and feature selection for Alzheimer's disease diagnosis. in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition., 6909791, IEEE Computer Society, pp. 3089-3096, 27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, Columbus, United States, 14/6/23. https://doi.org/10.1109/CVPR.2014.395
Zhu X, Suk H-I, Shen D. Matrix-similarity based loss function and feature selection for Alzheimer's disease diagnosis. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society. 2014. p. 3089-3096. 6909791 https://doi.org/10.1109/CVPR.2014.395
Zhu, Xiaofeng ; Suk, Heung-Il ; Shen, Dinggang. / Matrix-similarity based loss function and feature selection for Alzheimer's disease diagnosis. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 2014. pp. 3089-3096
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