{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T01:40:48Z","timestamp":1755826848478,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":38,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,5,30]],"date-time":"2024-05-30T00:00:00Z","timestamp":1717027200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100006374","name":"Beijing Municipal Science and Technology Commission","doi-asserted-by":"publisher","award":["No.Z221100002722020"],"award-info":[{"award-number":["No.Z221100002722020"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006374","name":"Royal Academy of Engineering","doi-asserted-by":"publisher","award":["INSILEX CiET1919\/19"],"award-info":[{"award-number":["INSILEX CiET1919\/19"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006374","name":"European Research Council","doi-asserted-by":"publisher","award":["INSILICO EP\/Y030494\/1"],"award-info":[{"award-number":["INSILICO EP\/Y030494\/1"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006374","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.62072045"],"award-info":[{"award-number":["No.62072045"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006374","name":"Natural Science Foundation of Beijing Municipality","doi-asserted-by":"publisher","award":["No.7242167"],"award-info":[{"award-number":["No.7242167"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,5,30]]},"DOI":"10.1145\/3652583.3658072","type":"proceedings-article","created":{"date-parts":[[2024,6,7]],"date-time":"2024-06-07T06:30:40Z","timestamp":1717741840000},"page":"248-256","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Vector-Aware Anisotropic Gauge Equivariant Mesh Convolution Network for 3D Aneurysm Detection"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1502-1712","authenticated-orcid":false,"given":"Xudong","family":"Ru","sequence":"first","affiliation":[{"name":"Beijing Normal University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4967-7533","authenticated-orcid":false,"given":"Haichuan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Beijing Normal University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3177-8902","authenticated-orcid":false,"given":"Xingce","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing Normal University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3735-6476","authenticated-orcid":false,"given":"Zhongke","family":"Wu","sequence":"additional","affiliation":[{"name":"Beijing Normal University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9265-0777","authenticated-orcid":false,"given":"Shaolong","family":"Liu","sequence":"additional","affiliation":[{"name":"Beijing Normal University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8966-1379","authenticated-orcid":false,"given":"Yi-Cheng","family":"Zhu","sequence":"additional","affiliation":[{"name":"Peking Union Medical College Hospital, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2675-528X","authenticated-orcid":false,"given":"Alejandro F.","family":"Frangi","sequence":"additional","affiliation":[{"name":"The University of Manchester &amp; KU Leuven, Manchester, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,6,7]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2021.06.013"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"crossref","first-page":"e000757","DOI":"10.1161\/SVIN.122.000757","article-title":"Imaging of Intracranial Saccular Aneurysms. Stroke","volume":"3","author":"Beaman Charles","year":"2023","unstructured":"Charles Beaman, Smit D. Patel, Kambiz Nael, Geoffrey P. Colby, and David S. Liebeskind. 2023. Imaging of Intracranial Saccular Aneurysms. Stroke: Vascular and Interventional Neurology, Vol. 3, 5 (2023), e000757.","journal-title":"Vascular and Interventional Neurology"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMra052760"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1097\/SLA.0000000000004711"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.106998"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neurad.2022.03.005"},{"key":"e_1_3_2_1_7_1","volume-title":"A general theory of equivariant cnns on homogeneous spaces. Advances in neural information processing systems","author":"Cohen Taco S","year":"2019","unstructured":"Taco S Cohen, Mario Geiger, and Maurice Weiler. 2019. A general theory of equivariant cnns on homogeneous spaces. Advances in neural information processing systems , Vol. 32 (2019)."},{"key":"e_1_3_2_1_8_1","volume-title":"International Conference on Learning Representations. 0--0.","author":"de Haan Pim","year":"2021","unstructured":"Pim de Haan, Maurice Weiler, Taco Cohen, and Max Welling. 2021. Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs. In International Conference on Learning Representations. 0--0."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2019.00509"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1161\/STR.0000000000000407"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2022.110457"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3306346.3322959","article-title":"Meshcnn: a network with an edge","volume":"38","author":"Hanocka Rana","year":"2019","unstructured":"Rana Hanocka, Amir Hertz, Noa Fish, Raja Giryes, Shachar Fleishman, and Daniel Cohen-Or. 2019. Meshcnn: a network with an edge. ACM Transactions on Graphics, Vol. 38, 4 (2019), 1--12.","journal-title":"ACM Transactions on Graphics"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01112"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3506694"},{"key":"e_1_3_2_1_15_1","volume-title":"Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.13699"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00979"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-16443-9_10"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2007.901008"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.25842"},{"key":"e_1_3_2_1_21_1","volume-title":"Advances in Neural Information Processing Systems","volume":"32","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Advances in Neural Information Processing Systems , Vol. 32 (2019)."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2009.2021652"},{"volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 652--660","author":"Qi Charles R.","key":"e_1_3_2_1_23_1","unstructured":"Charles R. Qi, Hao Su, Kaichun Mo, and Leonidas J. Guibas. 2017a. Pointnet: Deep learning on point sets for 3d classification and segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 652--660."},{"key":"e_1_3_2_1_24_1","volume-title":"Guibas","author":"Qi Charles Ruizhongtai","year":"2017","unstructured":"Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J. Guibas. 2017b. Pointnet: Deep hierarchical feature learning on point sets in a metric space. Advances in Neural Information Processing Systems , Vol. 30 (2017)."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.106372"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-007-0197-5"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-018-0162-z"},{"key":"e_1_3_2_1_28_1","volume-title":"Wolterink","author":"Suk Julian","year":"2022","unstructured":"Julian Suk, Pim de Haan, Phillip Lippe, Christoph Brune, and Jelmer M. Wolterink. 2022. Mesh Convolutional Neural Networks for Wall Shear Stress Estimation in 3D Artery Models. In Statistical Atlases and Computational Models of the Heart. Multi-Disease, Multi-View, and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge, Vol. 13131. 93."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2021.118216"},{"key":"e_1_3_2_1_30_1","volume-title":"Advanced feature learning on point clouds using multi-resolution features and learnable pooling. arXiv:2205.09962","author":"Wijaya Kevin Tirta","year":"2022","unstructured":"Kevin Tirta Wijaya, Dong-Hee Paek, and Seung-Hyun Kong. 2022. Advanced feature learning on point clouds using multi-resolution features and learnable pooling. arXiv:2205.09962 (2022)."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00985"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00273"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1007\/s41095-022-0270-z"},{"key":"e_1_3_2_1_34_1","volume-title":"3d medical point transformer: Introducing convolution to attention networks for medical point cloud analysis. arXiv:2112.04863","author":"Yu Jianhui","year":"2021","unstructured":"Jianhui Yu, Chaoyi Zhang, Heng Wang, Dingxin Zhang, Yang Song, Tiange Xiang, Dongnan Liu, and Weidong Cai. 2021. 3d medical point transformer: Introducing convolution to attention networks for medical point cloud analysis. arXiv:2112.04863 (2021)."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2951439"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-022-01601-z"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01595"},{"key":"e_1_3_2_1_38_1","volume-title":"CRANet: a comprehensive residual attention network for intracranial aneurysm image classification. BMC bioinformatics","author":"Zhao Yawu","year":"2022","unstructured":"Yawu Zhao, Shudong Wang, Yande Ren, and Yulin Zhang. 2022. CRANet: a comprehensive residual attention network for intracranial aneurysm image classification. BMC bioinformatics, Vol. 23, 1 (2022), 322."}],"event":{"name":"ICMR '24: International Conference on Multimedia Retrieval","sponsor":["SIGMM ACM Special Interest Group on Multimedia","SIGSOFT ACM Special Interest Group on Software Engineering"],"location":"Phuket Thailand","acronym":"ICMR '24"},"container-title":["Proceedings of the 2024 International Conference on Multimedia Retrieval"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3652583.3658072","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3652583.3658072","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T08:52:54Z","timestamp":1755766374000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3652583.3658072"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,30]]},"references-count":38,"alternative-id":["10.1145\/3652583.3658072","10.1145\/3652583"],"URL":"https:\/\/doi.org\/10.1145\/3652583.3658072","relation":{},"subject":[],"published":{"date-parts":[[2024,5,30]]},"assertion":[{"value":"2024-06-07","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}