3D convolutional neural network for feature extraction and classification of fMRI volumes

Hanh Vu; Hyun Chul Kim; Jong Hwan Lee

doi:10.1109/PRNI.2018.8423964

3D convolutional neural network for feature extraction and classification of fMRI volumes

Hanh Vu
, Hyun Chul Kim
, Jong Hwan Lee

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

11 Citations (Scopus)

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	https://doi.org/10.1109/PRNI.2018.8423964
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

Publication series

Name	2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018

Other

Other	2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018
Country/Territory	Singapore
City	Singapore
Period	18/6/12 → 18/6/14

Bibliographical note

Funding Information:
National Research Foundation (NRF)

Funding Information:
This work was supported by the National Research Foundation (NRF) grant, MSIP of Korea (NRF-2015R1A2A2A03004462, NRF-2017R1E1A1A01077288, NRF-2016M3C7A1914450), in part by the grant of the Ministry of Education of the Republic of Korea and the NRF of Korea (NRF-2015S1A5B6036594), in part by a grant of the Korean Health Technology R&D Project, Ministry of Health & Welfare, Korea (HI12C1847), and in part by the ICT R&D program of MSIP/IITP [R7124-16-0004, Development of Intelligent Interaction Technology Based on Context Awareness and Human Intention Understanding].

Publisher Copyright:
© 2018 IEEE.

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

Access to Document

10.1109/PRNI.2018.8423964

Cite this

Vu, H., Kim, H. C., & Lee, J. H. (2018). 3D convolutional neural network for feature extraction and classification of fMRI volumes. In 2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018 Article 8423964 (2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PRNI.2018.8423964

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 (2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018).

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

Vu, H, Kim, HC & Lee, JH 2018, 3D convolutional neural network for feature extraction and classification of fMRI volumes. in 2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018., 8423964, 2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018, Institute of Electrical and Electronics Engineers Inc., 2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018, Singapore, Singapore, 18/6/12. https://doi.org/10.1109/PRNI.2018.8423964

Vu H, Kim HC, Lee JH. 3D convolutional neural network for feature extraction and classification of fMRI volumes. In 2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018. Institute of Electrical and Electronics Engineers Inc. 2018. 8423964. (2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018). doi: 10.1109/PRNI.2018.8423964

@inproceedings{23c30faaff57440e822f0c7a516e29c3,

title = "3D convolutional neural network for feature extraction and classification of fMRI volumes",

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.",

keywords = "3D-CNN, Convolutional neural network (CNN), deep learning, fMRI, sensorimotor",

author = "Hanh Vu and Kim, \{Hyun Chul\} and Lee, \{Jong Hwan\}",

note = "Funding Information: National Research Foundation (NRF) Funding Information: This work was supported by the National Research Foundation (NRF) grant, MSIP of Korea (NRF-2015R1A2A2A03004462, NRF-2017R1E1A1A01077288, NRF-2016M3C7A1914450), in part by the grant of the Ministry of Education of the Republic of Korea and the NRF of Korea (NRF-2015S1A5B6036594), in part by a grant of the Korean Health Technology R\&D Project, Ministry of Health \& Welfare, Korea (HI12C1847), and in part by the ICT R\&D program of MSIP/IITP [R7124-16-0004, Development of Intelligent Interaction Technology Based on Context Awareness and Human Intention Understanding]. Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018 ; Conference date: 12-06-2018 Through 14-06-2018",

year = "2018",

month = jul,

day = "31",

doi = "10.1109/PRNI.2018.8423964",

language = "English",

isbn = "9781538668597",

series = "2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018",

}

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

N1 - Funding Information: National Research Foundation (NRF) Funding Information: This work was supported by the National Research Foundation (NRF) grant, MSIP of Korea (NRF-2015R1A2A2A03004462, NRF-2017R1E1A1A01077288, NRF-2016M3C7A1914450), in part by the grant of the Ministry of Education of the Republic of Korea and the NRF of Korea (NRF-2015S1A5B6036594), in part by a grant of the Korean Health Technology R&D Project, Ministry of Health & Welfare, Korea (HI12C1847), and in part by the ICT R&D program of MSIP/IITP [R7124-16-0004, Development of Intelligent Interaction Technology Based on Context Awareness and Human Intention Understanding]. Publisher Copyright: © 2018 IEEE.

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 - https://www.scopus.com/pages/publications/85051527795

U2 - 10.1109/PRNI.2018.8423964

DO - 10.1109/PRNI.2018.8423964

M3 - Conference contribution

AN - SCOPUS:85051527795

SN - 9781538668597

T3 - 2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018

BT - 2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018

Y2 - 12 June 2018 through 14 June 2018

ER -

3D convolutional neural network for feature extraction and classification of fMRI volumes

Abstract

Publication series

Other

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this