{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T15:58:52Z","timestamp":1765209532561,"version":"3.46.0"},"publisher-location":"New York, NY, USA","reference-count":26,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,11,17]],"date-time":"2023-11-17T00:00:00Z","timestamp":1700179200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"the Ministry of Science and Technology of China grant","award":["2023YFF0714204, 2020AAA0105601, 2019YFA0707103, 2022ZD0211901"],"award-info":[{"award-number":["2023YFF0714204, 2020AAA0105601, 2019YFA0707103, 2022ZD0211901"]}]},{"name":"the University Synergy Innovation Program of Anhui Province","award":["GXXT-2021-002, GXXT-2022-029"],"award-info":[{"award-number":["GXXT-2021-002, GXXT-2022-029"]}]},{"name":"the Youth Innovation Promotion Association CAS","award":["2021091, YSBR-068"],"award-info":[{"award-number":["2021091, YSBR-068"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,11,17]]},"DOI":"10.1145\/3652628.3652789","type":"proceedings-article","created":{"date-parts":[[2024,5,23]],"date-time":"2024-05-23T10:36:46Z","timestamp":1716460606000},"page":"969-975","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["CMG: Class and mask guided network for hippocampus and its subregions segmentation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-3640-8251","authenticated-orcid":false,"given":"Zimu","family":"Wang","sequence":"first","affiliation":[{"name":"AHU-IAI AI Joint Laboratory, Anhui University, China and \rInstitute of Artificial Intelligence, Hefei Comprehensive National Science Center, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5909-3884","authenticated-orcid":false,"given":"Zhentao","family":"Zuo","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, China and \rUniversity of Chinese Academy of Sciences, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0164-7944","authenticated-orcid":false,"given":"Dengdi","family":"Sun","sequence":"additional","affiliation":[{"name":"Anhui Provincial Key Laboratory of Multimodal Cognitive Computing, School of Artificial Intelligence, Anhui University, China, \rAHU-IAI AI Joint Laboratory, Anhui University, China and \rInstitute of Artificial Intelligence, Hefei Comprehensive National Science Center, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-1005-5106","authenticated-orcid":false,"given":"Tiangang","family":"Zhou","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, China, \rInstitute of Artificial Intelligence, Hefei Comprehensive National Science Center, China and \rUniversity of Chinese Academy of Sciences, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,5,23]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.3389\/fncir.2017.00086"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrn2614"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1001\/archpsyc.63.7.795"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0210641"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0896-6273(02)00569-X"},{"volume-title":"Computer methods and programs in biomedicine","author":"Cabezas M.","key":"e_1_3_2_1_6_1","unstructured":"M. Cabezas, A. Oliver, X. Llad\u00f3, J. Freixenet, and M. B. Cuadra, \u201cA review of atlas-based segmentation for magnetic resonance brain images,\u201d Computer methods and programs in biomedicine, vol. 104, no. 3, pp. e158\u2013e177, 2011."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12938-019-0623-8"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2018.02.005"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s44163-022-00022-8"},{"key":"e_1_3_2_1_10_1","first-page":"241","volume-title":"18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18","author":"Ronneberger O.","year":"2015","unstructured":"O. Ronneberger, P. Fischer, and T. Brox, \u201cU-net: Convolutional networks for biomedical image segmentation,\u201d in Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, Springer, 2015, pp. 234\u2013241."},{"key":"e_1_3_2_1_11_1","first-page":"571","volume-title":"conference on 3D vision (3DV)","author":"Milletari F.","year":"2016","unstructured":"F. Milletari, N. Navab, and S.-A. Ahmadi, \u201cV-net: Fully convolutional neural networks for volumetric medical image segmentation,\u201d in 2016 fourth international conference on 3D vision (3DV), Ieee, 2016, pp. 565\u2013571."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1117\/12.2681343"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"e_1_3_2_1_14_1","volume-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"Dosovitskiy A.","year":"2010","unstructured":"A. Dosovitskiy , \u201cAn image is worth 16x16 words: Transformers for image recognition at scale,\u201d arXiv preprint arXiv:2010.11929, 2020."},{"key":"e_1_3_2_1_15_1","volume-title":"Advances in neural information processing systems","author":"Vaswani A.","year":"2017","unstructured":"A. Vaswani , \u201cAttention is all you need,\u201d Advances in neural information processing systems, vol. 30, 2017."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-43901-8_40"},{"key":"e_1_3_2_1_18_1","first-page":"1460","volume-title":"conference on applications of computer vision (WACV)","author":"Wang P.","year":"2018","unstructured":"P. Wang , \u201cUnderstanding convolution for semantic segmentation,\u201d in 2018 IEEE winter conference on applications of computer vision (WACV), Ieee, 2018, pp. 1451\u20131460."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00745"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2013.05.041"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.21049"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2015.04.042"},{"key":"e_1_3_2_1_23_1","first-page":"119","volume-title":"24th International Conference, Strasbourg, France, September 27\u2013October 1, 2021, Proceedings, Part I 24","author":"Wang W.","year":"2021","unstructured":"W. Wang, C. Chen, M. Ding, H. Yu, S. Zha, and J. Li, \u201cTransbts: Multimodal brain tumor segmentation using transformer,\u201d in Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021: 24th International Conference, Strasbourg, France, September 27\u2013October 1, 2021, Proceedings, Part I 24, Springer, 2021, pp. 109\u2013119."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2023.3293771"},{"key":"e_1_3_2_1_25_1","volume-title":"3d ux-net: A large kernel volumetric convnet modernizing hierarchical transformer for medical image segmentation","author":"Lee H. H.","year":"2022","unstructured":"H. H. Lee, S. Bao, Y. Huo, and B. A. Landman, \u201c3d ux-net: A large kernel volumetric convnet modernizing hierarchical transformer for medical image segmentation,\u201d arXiv preprint arXiv:2209.15076, 2022."},{"key":"e_1_3_2_1_26_1","volume-title":"Monai: An open-source framework for deep learning in healthcare","author":"Cardoso M. J.","year":"2022","unstructured":"M. J. Cardoso , \u201cMonai: An open-source framework for deep learning in healthcare,\u201d arXiv preprint arXiv:2211.02701, 2022."}],"event":{"name":"ICAICE 2023: The 4th International Conference on Artificial Intelligence and Computer Engineering","acronym":"ICAICE 2023","location":"Dalian China"},"container-title":["Proceedings of the 4th International Conference on Artificial Intelligence and Computer Engineering"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3652628.3652789","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3652628.3652789","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T15:30:17Z","timestamp":1765207817000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3652628.3652789"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,17]]},"references-count":26,"alternative-id":["10.1145\/3652628.3652789","10.1145\/3652628"],"URL":"https:\/\/doi.org\/10.1145\/3652628.3652789","relation":{},"subject":[],"published":{"date-parts":[[2023,11,17]]},"assertion":[{"value":"2024-05-23","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}