Dynamic Image for 3D MRI Image Alzheimer’s Disease Classification

Xin Xing; Gongbo Liang; Hunter Blanton; Muhammad Usman Rafique; Chris Wang; Ai Ling Lin; Nathan Jacobs

doi:10.1007/978-3-030-66415-2_23

Dynamic Image for 3D MRI Image Alzheimer’s Disease Classification

Xin Xing
, Gongbo Liang
, Hunter Blanton
, Muhammad Usman Rafique
, Chris Wang
, Ai Ling Lin
, Nathan Jacobs

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

45 Scopus citations

Abstract

We propose to apply a 2D CNN architecture to 3D MRI image Alzheimer’s disease classification. Training a 3D convolutional neural network (CNN) is time-consuming and computationally expensive. We make use of approximate rank pooling to transform the 3D MRI image volume into a 2D image to use as input to a 2D CNN. We show our proposed CNN model achieves 9.5 % better Alzheimer’s disease classification accuracy than the baseline 3D models. We also show that our method allows for efficient training, requiring only 20 % of the training time compared to 3D CNN models. The code is available online: https://github.com/UkyVision/alzheimer-project.

Original language	English
Title of host publication	Computer Vision – ECCV 2020 Workshops, Proceedings
Editors	Adrien Bartoli, Andrea Fusiello
Pages	355-364
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-030-66415-2_23
State	Published - 2020
Event	Workshops held at the 16th European Conference on Computer Vision, ECCV 2020 - Glasgow, United Kingdom Duration: Aug 23 2020 → Aug 28 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12535 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	Workshops held at the 16th European Conference on Computer Vision, ECCV 2020
Country/Territory	United Kingdom
City	Glasgow
Period	8/23/20 → 8/28/20

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Funding

This work is supported by NIH/NIAR01AG054459.

Funders
NIAR01AG054459
National Institutes of Health (NIH)

Keywords

2D CNN
Alzheimer’s disease
Dynamic image
MRI image

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-66415-2_23

Cite this

Xing, X., Liang, G., Blanton, H., Rafique, M. U., Wang, C., Lin, A. L., & Jacobs, N. (2020). Dynamic Image for 3D MRI Image Alzheimer’s Disease Classification. In A. Bartoli, & A. Fusiello (Eds.), Computer Vision – ECCV 2020 Workshops, Proceedings (pp. 355-364). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12535 LNCS). https://doi.org/10.1007/978-3-030-66415-2_23

Xing, Xin ; Liang, Gongbo ; Blanton, Hunter et al. / Dynamic Image for 3D MRI Image Alzheimer’s Disease Classification. Computer Vision – ECCV 2020 Workshops, Proceedings. editor / Adrien Bartoli ; Andrea Fusiello. 2020. pp. 355-364 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{dd8aba8c82654e9e8cebe5ae93e502ee,

title = "Dynamic Image for 3D MRI Image Alzheimer{\textquoteright}s Disease Classification",

abstract = "We propose to apply a 2D CNN architecture to 3D MRI image Alzheimer{\textquoteright}s disease classification. Training a 3D convolutional neural network (CNN) is time-consuming and computationally expensive. We make use of approximate rank pooling to transform the 3D MRI image volume into a 2D image to use as input to a 2D CNN. We show our proposed CNN model achieves 9.5 \% better Alzheimer{\textquoteright}s disease classification accuracy than the baseline 3D models. We also show that our method allows for efficient training, requiring only 20 \% of the training time compared to 3D CNN models. The code is available online: https://github.com/UkyVision/alzheimer-project.",

keywords = "2D CNN, Alzheimer{\textquoteright}s disease, Dynamic image, MRI image",

author = "Xin Xing and Gongbo Liang and Hunter Blanton and Rafique, \{Muhammad Usman\} and Chris Wang and Lin, \{Ai Ling\} and Nathan Jacobs",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; Workshops held at the 16th European Conference on Computer Vision, ECCV 2020 ; Conference date: 23-08-2020 Through 28-08-2020",

year = "2020",

doi = "10.1007/978-3-030-66415-2\_23",

language = "English",

isbn = "9783030664145",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "355--364",

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booktitle = "Computer Vision – ECCV 2020 Workshops, Proceedings",

}

Xing, X, Liang, G, Blanton, H, Rafique, MU, Wang, C, Lin, AL & Jacobs, N 2020, Dynamic Image for 3D MRI Image Alzheimer’s Disease Classification. in A Bartoli & A Fusiello (eds), Computer Vision – ECCV 2020 Workshops, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12535 LNCS, pp. 355-364, Workshops held at the 16th European Conference on Computer Vision, ECCV 2020, Glasgow, United Kingdom, 8/23/20. https://doi.org/10.1007/978-3-030-66415-2_23

Dynamic Image for 3D MRI Image Alzheimer’s Disease Classification. / Xing, Xin; Liang, Gongbo; Blanton, Hunter et al.
Computer Vision – ECCV 2020 Workshops, Proceedings. ed. / Adrien Bartoli; Andrea Fusiello. 2020. p. 355-364 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12535 LNCS).

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

TY - GEN

T1 - Dynamic Image for 3D MRI Image Alzheimer’s Disease Classification

AU - Xing, Xin

AU - Liang, Gongbo

AU - Blanton, Hunter

AU - Rafique, Muhammad Usman

AU - Wang, Chris

AU - Lin, Ai Ling

AU - Jacobs, Nathan

PY - 2020

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N2 - We propose to apply a 2D CNN architecture to 3D MRI image Alzheimer’s disease classification. Training a 3D convolutional neural network (CNN) is time-consuming and computationally expensive. We make use of approximate rank pooling to transform the 3D MRI image volume into a 2D image to use as input to a 2D CNN. We show our proposed CNN model achieves 9.5 % better Alzheimer’s disease classification accuracy than the baseline 3D models. We also show that our method allows for efficient training, requiring only 20 % of the training time compared to 3D CNN models. The code is available online: https://github.com/UkyVision/alzheimer-project.

AB - We propose to apply a 2D CNN architecture to 3D MRI image Alzheimer’s disease classification. Training a 3D convolutional neural network (CNN) is time-consuming and computationally expensive. We make use of approximate rank pooling to transform the 3D MRI image volume into a 2D image to use as input to a 2D CNN. We show our proposed CNN model achieves 9.5 % better Alzheimer’s disease classification accuracy than the baseline 3D models. We also show that our method allows for efficient training, requiring only 20 % of the training time compared to 3D CNN models. The code is available online: https://github.com/UkyVision/alzheimer-project.

KW - 2D CNN

KW - Alzheimer’s disease

KW - Dynamic image

KW - MRI image

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M3 - Conference contribution

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

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BT - Computer Vision – ECCV 2020 Workshops, Proceedings

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ER -

Xing X, Liang G, Blanton H, Rafique MU, Wang C, Lin AL et al. Dynamic Image for 3D MRI Image Alzheimer’s Disease Classification. In Bartoli A, Fusiello A, editors, Computer Vision – ECCV 2020 Workshops, Proceedings. 2020. p. 355-364. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-66415-2_23

Dynamic Image for 3D MRI Image Alzheimer’s Disease Classification

Abstract

Publication series

Conference

Bibliographical note

Funding

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this