Abstract
Recently, deep learning (DL) techniques have been gaining interest in the neuroimaging community. In this study, we present 3D convolutional neural network (3D-CNN) as an end-To-end model to label a target task among four sensorimotor tasks for each functional magnetic resonance imaging (fMRI) volume. To the best of our knowledge, this is the first study that employs a single blood-oxygenation-level-dependent (BOLD) fMRI volume as the input of the 3D-CNN for task classification. We hypothesized that 3D-CNN has the capability to extract potentially shift-invariant features in local brain areas while preserving the overall spatial layout of the whole brain fMRI volume. We designed a 3D-CNN model by extending the LeNet-5 CNN for 2D image classification to 3D volume classification. The designed 3D-CNN model was thoroughly evaluated using BOLD fMRI volumes acquired from four sensorimotor tasks in terms of the classification performance and feature representations for each of the four sensorimotor tasks.
| Original language | English |
|---|---|
| Title of host publication | 2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Print) | 9781538668597 |
| DOIs | |
| Publication status | Published - 2018 Jul 31 |
| Event | 2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018 - Singapore, Singapore Duration: 2018 Jun 12 → 2018 Jun 14 |
Other
| Other | 2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018 |
|---|---|
| Country | Singapore |
| City | Singapore |
| Period | 18/6/12 → 18/6/14 |
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Keywords
- 3D-CNN
- Convolutional neural network (CNN)
- deep learning
- fMRI
- sensorimotor
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Radiology Nuclear Medicine and imaging
- Behavioral Neuroscience
- Cognitive Neuroscience
- Neurology
Cite this
3D convolutional neural network for feature extraction and classification of fMRI volumes. / Vu, Hanh; Kim, Hyun Chul; Lee, Jong-Hwan.
2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018. Institute of Electrical and Electronics Engineers Inc., 2018. 8423964.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - 3D convolutional neural network for feature extraction and classification of fMRI volumes
AU - Vu, Hanh
AU - Kim, Hyun Chul
AU - Lee, Jong-Hwan
PY - 2018/7/31
Y1 - 2018/7/31
N2 - Recently, deep learning (DL) techniques have been gaining interest in the neuroimaging community. In this study, we present 3D convolutional neural network (3D-CNN) as an end-To-end model to label a target task among four sensorimotor tasks for each functional magnetic resonance imaging (fMRI) volume. To the best of our knowledge, this is the first study that employs a single blood-oxygenation-level-dependent (BOLD) fMRI volume as the input of the 3D-CNN for task classification. We hypothesized that 3D-CNN has the capability to extract potentially shift-invariant features in local brain areas while preserving the overall spatial layout of the whole brain fMRI volume. We designed a 3D-CNN model by extending the LeNet-5 CNN for 2D image classification to 3D volume classification. The designed 3D-CNN model was thoroughly evaluated using BOLD fMRI volumes acquired from four sensorimotor tasks in terms of the classification performance and feature representations for each of the four sensorimotor tasks.
AB - Recently, deep learning (DL) techniques have been gaining interest in the neuroimaging community. In this study, we present 3D convolutional neural network (3D-CNN) as an end-To-end model to label a target task among four sensorimotor tasks for each functional magnetic resonance imaging (fMRI) volume. To the best of our knowledge, this is the first study that employs a single blood-oxygenation-level-dependent (BOLD) fMRI volume as the input of the 3D-CNN for task classification. We hypothesized that 3D-CNN has the capability to extract potentially shift-invariant features in local brain areas while preserving the overall spatial layout of the whole brain fMRI volume. We designed a 3D-CNN model by extending the LeNet-5 CNN for 2D image classification to 3D volume classification. The designed 3D-CNN model was thoroughly evaluated using BOLD fMRI volumes acquired from four sensorimotor tasks in terms of the classification performance and feature representations for each of the four sensorimotor tasks.
KW - 3D-CNN
KW - Convolutional neural network (CNN)
KW - deep learning
KW - fMRI
KW - sensorimotor
UR - http://www.scopus.com/inward/record.url?scp=85051527795&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85051527795&partnerID=8YFLogxK
U2 - 10.1109/PRNI.2018.8423964
DO - 10.1109/PRNI.2018.8423964
M3 - Conference contribution
AN - SCOPUS:85051527795
SN - 9781538668597
BT - 2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018
PB - Institute of Electrical and Electronics Engineers Inc.
ER -