{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T01:13:29Z","timestamp":1740100409247,"version":"3.37.3"},"reference-count":27,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,8,23]],"date-time":"2021-08-23T00:00:00Z","timestamp":1629676800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,8,23]],"date-time":"2021-08-23T00:00:00Z","timestamp":1629676800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100011878","name":"Flemish Government","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100011878","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,8,23]]},"DOI":"10.23919\/eusipco54536.2021.9616349","type":"proceedings-article","created":{"date-parts":[[2021,12,8]],"date-time":"2021-12-08T21:55:53Z","timestamp":1639000553000},"page":"1236-1240","source":"Crossref","is-referenced-by-count":1,"title":["Automatic Artifact Removal of Resting-State fMRI with Deep Neural Networks"],"prefix":"10.23919","author":[{"given":"Christos","family":"Theodoropoulos","sequence":"first","affiliation":[{"name":"Department of Computer Science, LIIR Lab, KU,Leuven,Belgium"}]},{"given":"Christos","family":"Chatzichristos","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, STADIUS, KU,Leuven,Belgium"}]},{"given":"Sabine","family":"Van Huffel","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, STADIUS, KU,Leuven,Belgium"}]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2019.05.043"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/s12021-019-09419-w"},{"key":"ref12","article-title":"Deep learning","volume":"1","author":"goodfellow","year":"2016","journal-title":"MIT Press Cambridge"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32248-9_84"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2018.03.049"},{"key":"ref15","article-title":"Convolutional networks for images, speech, and time series","author":"lecun","year":"1995","journal-title":"The Handbook of Brain Theory and Neural Networks"},{"key":"ref16","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"ioffe","year":"2015","journal-title":"ArXiv Preprint"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2016.7477624"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1049\/trit.2018.1054"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2018.12.007"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"journal-title":"Machine Learning A Bayesian and Optimization Perspective","year":"2015","author":"theodoridis","key":"ref3"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2013.11.046"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2008.10.057"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2003.822821"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2014.03.034"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2015.03.070"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/0169-7439(87)80084-9"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1214\/09-STS282"},{"key":"ref20","article-title":"Inception-v4, inception-resnet and the impact of residual connections on learning","author":"szegedy","year":"2016","journal-title":"ArXiv Preprint"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2018.8363676"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2013.05.039"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.ebiom.2019.08.023"},{"key":"ref26","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014","journal-title":"ArXiv Preprint"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2013.05.041"}],"event":{"name":"2021 29th European Signal Processing Conference (EUSIPCO)","start":{"date-parts":[[2021,8,23]]},"location":"Dublin, Ireland","end":{"date-parts":[[2021,8,27]]}},"container-title":["2021 29th European Signal Processing Conference (EUSIPCO)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9615915\/9615917\/09616349.pdf?arnumber=9616349","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,3]],"date-time":"2022-08-03T00:14:48Z","timestamp":1659485688000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9616349\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,23]]},"references-count":27,"URL":"https:\/\/doi.org\/10.23919\/eusipco54536.2021.9616349","relation":{},"subject":[],"published":{"date-parts":[[2021,8,23]]}}}