Deep attentive spatio-temporal feature learning for automatic resting-state fMRI denoising.

Heo, Keun-Soo; Shin, Dong-Hee; Hung, Sheng-Che; Lin, Weili; Zhang, Han; Shen, Dinggang; Kam, Tae-Eui

Heo, Keun-Soo; Shin, Dong-Hee; Hung, Sheng-Che; Lin, Weili; Zhang, Han; Shen, Dinggang; Kam, Tae-Eui.

Affiliation

Heo KS; Department of Artificial Intelligence, Korea University, Seoul, Republic of Korea.
Shin DH; Department of Artificial Intelligence, Korea University, Seoul, Republic of Korea.
Hung SC; Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA.
Lin W; Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA.
Zhang H; School of Biomedical Engineering, ShanghaiTech University, Shanghai, China.
Shen D; School of Biomedical Engineering, ShanghaiTech University, Shanghai, China.
Kam TE; Department of Artificial Intelligence, Korea University, Seoul, Republic of Korea. Electronic address: kamte@korea.ac.kr.

Neuroimage ; 254: 119127, 2022 07 01.

Article in En | MEDLINE | ID: mdl-35337965

Subject(s)
Key words

Convolutional neural network; Deep learning; Denoising; Independent component analysis; Resting-state fMRI

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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Magnetic Resonance Imaging / Connectome Type of study: Guideline Limits: Adult / Humans Language: En Journal: Neuroimage Journal subject: DIAGNOSTICO POR IMAGEM Year: 2022 Document type: Article Country of publication: Estados Unidos

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