{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T01:10:10Z","timestamp":1740100210054,"version":"3.37.3"},"reference-count":30,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T00:00:00Z","timestamp":1626566400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T00:00:00Z","timestamp":1626566400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61672510,61950410615"],"award-info":[{"award-number":["61672510,61950410615"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017607","name":"Shenzhen Basic Research Program","doi-asserted-by":"publisher","award":["JCYJ20180507182441903"],"award-info":[{"award-number":["JCYJ20180507182441903"]}],"id":[{"id":"10.13039\/501100017607","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,7,18]]},"DOI":"10.1109\/ijcnn52387.2021.9534214","type":"proceedings-article","created":{"date-parts":[[2021,9,22]],"date-time":"2021-09-22T20:32:37Z","timestamp":1632342757000},"page":"1-8","source":"Crossref","is-referenced-by-count":3,"title":["Multiple Self-attention Network for Intracranial Vessel Segmentation"],"prefix":"10.1109","author":[{"given":"Yang","family":"Li","sequence":"first","affiliation":[{"name":"Laboratory for Medical Imaging and Digital Surgery, Shenzhen Institutes of Advanced Technology, Chinese Academy of Science,Shenzhen,China"}]},{"given":"Jiajia","family":"Ni","sequence":"additional","affiliation":[{"name":"Laboratory for Medical Imaging and Digital Surgery, Shenzhen Institutes of Advanced Technology, Chinese Academy of Science,Shenzhen,China"}]},{"given":"Ahmed","family":"Elazab","sequence":"additional","affiliation":[{"name":"Shenzhen University,School of Biomedical Engineering,Shenzhen,China"}]},{"given":"Jianhuang","family":"Wu","sequence":"additional","affiliation":[{"name":"Laboratory for Medical Imaging and Digital Surgery, Shenzhen Institutes of Advanced Technology, Chinese Academy of Science,Shenzhen,China"}]}],"member":"263","reference":[{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-67558-9_28"},{"key":"ref10","article-title":"Semantic image segmentation with deep convolutional nets and fully connected crfs","author":"chen","year":"2014","journal-title":"ArXiv Preprint"},{"key":"ref11","article-title":"Conditional random fields as recurrent neural networks for 3d medical imaging segmentation","author":"monteiro","year":"2018","journal-title":"ArXiv Preprint"},{"key":"ref12","first-page":"424","article-title":"3d u-net: learning dense volumetric segmentation from sparse annotation","author":"\u00e7i\u00e7ek","year":"2016","journal-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/3DV.2016.79"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"ref15","article-title":"Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation","author":"alom","year":"2018","journal-title":"ArXiv Preprint"},{"key":"ref16","article-title":"Attention u-net: Learning where to look for the pancreas","author":"oktay","year":"2018","journal-title":"ArXiv Preprint"},{"key":"ref17","first-page":"5998","article-title":"Attention is all you need","volume":"30","author":"vaswani","year":"0","journal-title":"Advances in neural information processing systems"},{"key":"ref18","article-title":"Bert: Pre-training of deep bidirectional transformers for language understanding","author":"devlin","year":"2018","journal-title":"ArXiv Preprint"},{"key":"ref19","article-title":"Image transformer","author":"parmar","year":"2018","journal-title":"ArXiv Preprint"},{"key":"ref28","first-page":"2048","article-title":"Show, attend and tell: Neural image caption generation with visual attention","author":"xu","year":"0","journal-title":"International Conference on Machine Learning"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553469"},{"key":"ref27","first-page":"801","article-title":"Encoder-decoder with atrous separable convolution for semantic image segmentation","author":"chen","year":"0","journal-title":"Proceedings of the European Conference on Computer Vision (ECCV)"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-bioeng-071516-044442"},{"key":"ref6","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"ronneberger","year":"2015","journal-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D15-1166"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref7","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2014","journal-title":"ArXiv Preprint"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/MCI.2018.2840738"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46723-8_48"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neurad.2018.03.003"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"ref22","article-title":"Stitcher: Feedback-driven data provider for object detection","author":"chen","year":"2020","journal-title":"ArXiv Preprint"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2644615"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.549"}],"event":{"name":"2021 International Joint Conference on Neural Networks (IJCNN)","start":{"date-parts":[[2021,7,18]]},"location":"Shenzhen, China","end":{"date-parts":[[2021,7,22]]}},"container-title":["2021 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9533266\/9533267\/09534214.pdf?arnumber=9534214","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,2]],"date-time":"2022-08-02T23:33:02Z","timestamp":1659483182000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9534214\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,18]]},"references-count":30,"URL":"https:\/\/doi.org\/10.1109\/ijcnn52387.2021.9534214","relation":{},"subject":[],"published":{"date-parts":[[2021,7,18]]}}}