@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 = "DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.; 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",
editor = "Adrien Bartoli and Andrea Fusiello",
booktitle = "Computer Vision – ECCV 2020 Workshops, Proceedings",
}