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 proceedingConference contributionpeer-review

29 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 languageEnglish
Title of host publicationComputer Vision – ECCV 2020 Workshops, Proceedings
EditorsAdrien Bartoli, Andrea Fusiello
Pages355-364
Number of pages10
DOIs
StatePublished - 2020
EventWorkshops held at the 16th European Conference on Computer Vision, ECCV 2020 - Glasgow, United Kingdom
Duration: Aug 23 2020Aug 28 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12535 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceWorkshops held at the 16th European Conference on Computer Vision, ECCV 2020
Country/TerritoryUnited Kingdom
CityGlasgow
Period8/23/208/28/20

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Keywords

  • 2D CNN
  • Alzheimer’s disease
  • Dynamic image
  • MRI image

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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