Prediction of progression to Alzheimer's disease with deep infomax

Alex Fedorov; R. Devon Hjelm; Anees Abrol; Zening Fu; Yuhui Du; Sergey Plis; Vince D. Calhoun

doi:10.1109/BHI.2019.8834630

Prediction of progression to Alzheimer's disease with deep infomax

Alex Fedorov, R. Devon Hjelm, Anees Abrol, Zening Fu, Yuhui Du, Sergey Plis, Vince D. Calhoun

School of Medicine

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

4 Scopus citations

Abstract

Arguably, unsupervised learning plays a crucial role in the majority of algorithms for processing brain imaging. A recently introduced unsupervised approach Deep InfoMax (DIM) is a promising tool for exploring brain structure in a flexible non-linear way. In this paper, we investigate the use of variants of DIM in a setting of progression to Alzheimer's disease in comparison with supervised AlexNet and ResNet inspired convolutional neural networks. As a benchmark, we use a classification task between four groups: patients with stable, and progressive mild cognitive impairment (MCI), with Alzheimer's disease, and healthy controls. Our dataset is comprised of 828 subjects from the Alzheimers Disease Neuroimaging Initiative (ADNI) database. Our experiments highlight encouraging evidence of the high potential utility of DIM in future neuroimaging studies.

Original language	English (US)
Title of host publication	2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728108483
DOIs	https://doi.org/10.1109/BHI.2019.8834630
State	Published - May 2019
Event	2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Chicago, United States Duration: May 19 2019 → May 22 2019

Publication series

Name	2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings

Conference

Conference	2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019
Country/Territory	United States
City	Chicago
Period	5/19/19 → 5/22/19

Keywords

CNN
Classification
Deep InfoMax
MRI
Unsupervised

ASJC Scopus subject areas

Artificial Intelligence
Signal Processing
Information Systems and Management
Biomedical Engineering
Health Informatics
Radiology Nuclear Medicine and imaging

Access to Document

10.1109/BHI.2019.8834630

Cite this

Fedorov, A., Hjelm, R. D., Abrol, A., Fu, Z., Du, Y., Plis, S., & Calhoun, V. D. (2019). Prediction of progression to Alzheimer's disease with deep infomax. In 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings Article 8834630 (2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BHI.2019.8834630

Prediction of progression to Alzheimer's disease with deep infomax. / Fedorov, Alex; Hjelm, R. Devon; Abrol, Anees et al.
2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. 8834630 (2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings).

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

Fedorov, A, Hjelm, RD, Abrol, A, Fu, Z, Du, Y, Plis, S & Calhoun, VD 2019, Prediction of progression to Alzheimer's disease with deep infomax. in 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings., 8834630, 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019, Chicago, United States, 5/19/19. https://doi.org/10.1109/BHI.2019.8834630

Fedorov A, Hjelm RD, Abrol A, Fu Z, Du Y, Plis S et al. Prediction of progression to Alzheimer's disease with deep infomax. In 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. 8834630. (2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings). doi: 10.1109/BHI.2019.8834630

Fedorov, Alex ; Hjelm, R. Devon ; Abrol, Anees et al. / Prediction of progression to Alzheimer's disease with deep infomax. 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. (2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings).

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abstract = "Arguably, unsupervised learning plays a crucial role in the majority of algorithms for processing brain imaging. A recently introduced unsupervised approach Deep InfoMax (DIM) is a promising tool for exploring brain structure in a flexible non-linear way. In this paper, we investigate the use of variants of DIM in a setting of progression to Alzheimer's disease in comparison with supervised AlexNet and ResNet inspired convolutional neural networks. As a benchmark, we use a classification task between four groups: patients with stable, and progressive mild cognitive impairment (MCI), with Alzheimer's disease, and healthy controls. Our dataset is comprised of 828 subjects from the Alzheimers Disease Neuroimaging Initiative (ADNI) database. Our experiments highlight encouraging evidence of the high potential utility of DIM in future neuroimaging studies.",

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author = "Alex Fedorov and Hjelm, {R. Devon} and Anees Abrol and Zening Fu and Yuhui Du and Sergey Plis and Calhoun, {Vince D.}",

note = "Funding Information: ACKNOWLEDGEMENT This study is supported by NIH grants R01EB020407, R01EB006841, P20GM103472, P30GM122734. Data collection and sharing for this project was funded by the Alzheimer{\textquoteright}s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 ; Conference date: 19-05-2019 Through 22-05-2019",

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AU - Plis, Sergey

AU - Calhoun, Vince D.

N1 - Funding Information: ACKNOWLEDGEMENT This study is supported by NIH grants R01EB020407, R01EB006841, P20GM103472, P30GM122734. Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD Publisher Copyright: © 2019 IEEE.

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N2 - Arguably, unsupervised learning plays a crucial role in the majority of algorithms for processing brain imaging. A recently introduced unsupervised approach Deep InfoMax (DIM) is a promising tool for exploring brain structure in a flexible non-linear way. In this paper, we investigate the use of variants of DIM in a setting of progression to Alzheimer's disease in comparison with supervised AlexNet and ResNet inspired convolutional neural networks. As a benchmark, we use a classification task between four groups: patients with stable, and progressive mild cognitive impairment (MCI), with Alzheimer's disease, and healthy controls. Our dataset is comprised of 828 subjects from the Alzheimers Disease Neuroimaging Initiative (ADNI) database. Our experiments highlight encouraging evidence of the high potential utility of DIM in future neuroimaging studies.

AB - Arguably, unsupervised learning plays a crucial role in the majority of algorithms for processing brain imaging. A recently introduced unsupervised approach Deep InfoMax (DIM) is a promising tool for exploring brain structure in a flexible non-linear way. In this paper, we investigate the use of variants of DIM in a setting of progression to Alzheimer's disease in comparison with supervised AlexNet and ResNet inspired convolutional neural networks. As a benchmark, we use a classification task between four groups: patients with stable, and progressive mild cognitive impairment (MCI), with Alzheimer's disease, and healthy controls. Our dataset is comprised of 828 subjects from the Alzheimers Disease Neuroimaging Initiative (ADNI) database. Our experiments highlight encouraging evidence of the high potential utility of DIM in future neuroimaging studies.

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Prediction of progression to Alzheimer's disease with deep infomax

Abstract

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Conference

Keywords

ASJC Scopus subject areas

Access to Document

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