{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,6]],"date-time":"2025-03-06T14:40:13Z","timestamp":1741272013534,"version":"3.38.0"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T00:00:00Z","timestamp":1736208000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T00:00:00Z","timestamp":1736208000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Shenzhen Science and Technology Program","award":["No. JCYJ20200109114244249"],"award-info":[{"award-number":["No. JCYJ20200109114244249"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 52275565"],"award-info":[{"award-number":["No. 52275565"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NSF of Guangdong province","award":["2022A1515011667"],"award-info":[{"award-number":["2022A1515011667"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1007\/s00521-024-10824-9","type":"journal-article","created":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T04:23:17Z","timestamp":1736223797000},"page":"5927-5943","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["TSUBF-Net: Trans-spatial UNet-like network with Bi-direction fusion for segmentation of adenoid hypertrophy in CT"],"prefix":"10.1007","volume":"37","author":[{"given":"Rulin","family":"Zhou","sequence":"first","affiliation":[]},{"given":"Yingjie","family":"Feng","sequence":"additional","affiliation":[]},{"given":"Guankun","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xiaopin","family":"Zhong","sequence":"additional","affiliation":[]},{"given":"Zongze","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Qiang","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Xi","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,7]]},"reference":[{"key":"10824_CR1","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.smrv.2017.06.001","volume":"38","author":"L Pereira","year":"2018","unstructured":"Pereira L, Monyror J, Almeida FT, Almeida FR, Guerra E, Flores-Mir C, Pach\u00eaco-Pereira C (2018) Prevalence of adenoid hypertrophy: a systematic review and meta-analysis. Sleep Med Rev 38:101\u2013112. https:\/\/doi.org\/10.1016\/j.smrv.2017.06.001","journal-title":"Sleep Med Rev"},{"issue":"5","key":"10824_CR2","doi-asserted-by":"publisher","first-page":"426","DOI":"10.1093\/ejo\/cjm055","volume":"29","author":"T Peltom\u00e4ki","year":"2007","unstructured":"Peltom\u00e4ki T (2007) The effect of mode of breathing on craniofacial growth-revisited. Eur J Orthodont 29(5):426\u2013429","journal-title":"Eur J Orthodont"},{"key":"10824_CR3","doi-asserted-by":"publisher","first-page":"111310","DOI":"10.1016\/j.ijporl.2022.111310","volume":"162","author":"C Lu","year":"2022","unstructured":"Lu C, Sun C, Xu Y, Chen C, Li Q (2022) Polysomnography findings in preschool children with obstructive sleep apnea are affected by growth and developmental level. Int J Pediatr Otorhinolaryngol 162:111310","journal-title":"Int J Pediatr Otorhinolaryngol"},{"issue":"8","key":"10824_CR4","doi-asserted-by":"publisher","first-page":"1095","DOI":"10.1111\/j.1399-3038.2010.01012.x","volume":"21","author":"G Scadding","year":"2010","unstructured":"Scadding G (2010) Non-surgical treatment of adenoidal hypertrophy: the role of treating IGE-mediated inflammation. Pediatr Allergy Immunol 21(8):1095\u20131106","journal-title":"Pediatr Allergy Immunol"},{"issue":"4","key":"10824_CR5","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1016\/j.ajodo.2014.06.013","volume":"146","author":"MP Major","year":"2014","unstructured":"Major MP, Witmans M, El-Hakim H, Major PW, Flores-Mir C (2014) Agreement between cone-beam computed tomography and nasoendoscopy evaluations of adenoid hypertrophy. Am J Orthod Dentofac Orthop 146(4):451\u2013459","journal-title":"Am J Orthod Dentofac Orthop"},{"issue":"6","key":"10824_CR6","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1177\/000348940311200605","volume":"112","author":"JJ Shin","year":"2003","unstructured":"Shin JJ, Hartnick CJ (2003) Pediatric endoscopic transnasal adenoid ablation. Ann Otol Rhinol Laryngol 112(6):511\u2013514","journal-title":"Ann Otol Rhinol Laryngol"},{"issue":"2","key":"10824_CR7","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1177\/014556139707600204","volume":"76","author":"E Yanagisawa","year":"1997","unstructured":"Yanagisawa E, Weaver EM (1997) Endoscopic adenoidectomy with the microdebrider. Ear Nose Throat J 76(2):72\u201374","journal-title":"Ear Nose Throat J"},{"key":"10824_CR8","doi-asserted-by":"publisher","first-page":"910","DOI":"10.1007\/s12098-020-03203-4","volume":"87","author":"Y Wang","year":"2020","unstructured":"Wang Y, Jiao H, Mi C, Yang G, Han T (2020) Evaluation of adenoid hypertrophy with ultrasonography. Indian J Pediatr 87:910\u2013915","journal-title":"Indian J Pediatr"},{"issue":"8","key":"10824_CR9","doi-asserted-by":"publisher","first-page":"1386","DOI":"10.3390\/diagnostics11081386","volume":"11","author":"T Zhao","year":"2021","unstructured":"Zhao T, Zhou J, Yan J, Cao L, Cao Y, Hua F, He H (2021) Automated adenoid hypertrophy assessment with lateral cephalometry in children based on artificial intelligence. Diagnostics 11(8):1386","journal-title":"Diagnostics"},{"issue":"5","key":"10824_CR10","doi-asserted-by":"publisher","first-page":"2171","DOI":"10.1002\/mp.14063","volume":"47","author":"Y Shen","year":"2020","unstructured":"Shen Y, Li X, Liang X, Xu H, Li C, Yu Y, Qiu B (2020) A deep-learning-based approach for adenoid hypertrophy diagnosis. Med Phys 47(5):2171\u20132181","journal-title":"Med Phys"},{"key":"10824_CR11","doi-asserted-by":"crossref","unstructured":"Liang Z, Zhou Y, Ding L, Chen X (2022) An efficient deep model for children sleep apnea detection using snoring signals. In: Proceedings of the 3rd international symposium on artificial intelligence for medicine sciences, pp 551\u2013558","DOI":"10.1145\/3570773.3570862"},{"key":"10824_CR12","doi-asserted-by":"crossref","unstructured":"Zheng S, Li X, Bi M, Wang Y, Liu H, Feng X, Fan Y, Shen L (2022) Contrastive learning-based adenoid hypertrophy grading network using nasoendoscopic image. In: 2022 IEEE 35th international symposium on computer-based medical systems (CBMS). IEEE, pp 377\u2013382","DOI":"10.1109\/CBMS55023.2022.00074"},{"key":"10824_CR13","doi-asserted-by":"crossref","unstructured":"Alshbishiri AA, Marghalani MA, Khan HA, Ahmad RG, Alqarni MA, Khan MM (2021) Adenoid segmentation in x-ray images using u-net. In: 2021 national computing colleges conference (NCCC). IEEE, pp 1\u20136","DOI":"10.1109\/NCCC49330.2021.9428866"},{"key":"10824_CR14","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention\u2013MICCAI 2015: 18th international conference, Munich, Germany, October 5\u20139, 2015, Proceedings, Part III 18. Springer, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"10824_CR15","doi-asserted-by":"crossref","unstructured":"Wang G, Ren T-A, Lai J, Bai L, Ren H (2023) Domain adaptive sim-to-real segmentation of oropharyngeal organs. arXiv preprint arXiv:2305.10883","DOI":"10.1007\/s11517-023-02877-0"},{"key":"10824_CR16","doi-asserted-by":"publisher","first-page":"107412","DOI":"10.1016\/j.compbiomed.2023.107412","volume":"165","author":"G Wang","year":"2023","unstructured":"Wang G, Bai L, Wu Y, Chen T, Ren H (2023) Rethinking exemplars for continual semantic segmentation in endoscopy scenes: entropy-based mini-batch pseudo-replay. Comput Biol Med 165:107412","journal-title":"Comput Biol Med"},{"key":"10824_CR17","doi-asserted-by":"crossref","unstructured":"Huang H, Lin L, Tong R, Hu H, Zhang Q, Iwamoto Y, Han X, Chen Y-W, Wu J (2020) Unet 3+: a full-scale connected unet for medical image segmentation. In: ICASSP 2020-2020 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 1055\u20131059","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"key":"10824_CR18","doi-asserted-by":"crossref","unstructured":"Zhou Z, Rahman\u00a0Siddiquee MM, Tajbakhsh N, Liang J (2018) Unet++: a nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support: 4th international workshop, DLMIA 2018, and 8th international workshop, ML-CDS 2018, held in conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 4. Springer, pp 3\u201311","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"10824_CR19","doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek \u00d6, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O (2016) 3d u-net: learning dense volumetric segmentation from sparse annotation. In: Medical image computing and computer-assisted intervention\u2013MICCAI 2016: 19th international conference, Athens, Greece, October 17\u201321, 2016, Proceedings, Part II 19. Springer, pp 424\u2013432","DOI":"10.1007\/978-3-319-46723-8_49"},{"issue":"2","key":"10824_CR20","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee F, Jaeger PF, Kohl SA, Petersen J, Maier-Hein KH (2021) nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods 18(2):203\u2013211","journal-title":"Nat Methods"},{"key":"10824_CR21","unstructured":"Roth HR, Oda H, Hayashi Y, Oda M, Shimizu N, Fujiwara M, Misawa K, Mori K (2017) Hierarchical 3d fully convolutional networks for multi-organ segmentation. arXiv preprint arXiv:1704.06382"},{"key":"10824_CR22","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/j.media.2019.01.012","volume":"53","author":"J Schlemper","year":"2019","unstructured":"Schlemper J, Oktay O, Schaap M, Heinrich M, Kainz B, Glocker B, Rueckert D (2019) Attention gated networks: learning to leverage salient regions in medical images. Med Image Anal 53:197\u2013207","journal-title":"Med Image Anal"},{"issue":"5","key":"10824_CR23","doi-asserted-by":"publisher","first-page":"1316","DOI":"10.1109\/TMI.2019.2948320","volume":"39","author":"H Seo","year":"2019","unstructured":"Seo H, Huang C, Bassenne M, Xiao R, Xing L (2019) Modified u-net (mu-net) with incorporation of object-dependent high level features for improved liver and liver-tumor segmentation in ct images. IEEE Trans Med Imaging 39(5):1316\u20131325","journal-title":"IEEE Trans Med Imaging"},{"key":"10824_CR24","doi-asserted-by":"crossref","unstructured":"Xie Z, Zhao K, Yan X, Wu S, Mei J, Lu H (2022) Merged u-net for bone tumors x-ray images segmentation. In: 2022 IEEE international conference on image processing (ICIP). IEEE, pp 1276\u20131280","DOI":"10.1109\/ICIP46576.2022.9897539"},{"key":"10824_CR25","doi-asserted-by":"crossref","unstructured":"Chen L-C, Zhu Y, Papandreou G, Schroff F, Adam H (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European conference on computer vision (ECCV), pp 801\u2013818","DOI":"10.1007\/978-3-030-01234-2_49"},{"issue":"1","key":"10824_CR26","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.acra.2023.04.028","volume":"31","author":"KM Sunnetci","year":"2024","unstructured":"Sunnetci KM, Kaba E, Celiker FB, Alkan A (2024) Deep network-based comprehensive parotid gland tumor detection. Acad Radiol 31(1):157\u2013167. https:\/\/doi.org\/10.1016\/j.acra.2023.04.028","journal-title":"Acad Radiol"},{"key":"10824_CR27","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S et al (2020) An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929"},{"key":"10824_CR28","unstructured":"Wu B, Xu C, Dai X, Wan A, Zhang P, Yan Z, Tomizuka M, Gonzalez J, Keutzer K, Vajda P (2020) Visual transformers: token-based image representation and processing for computer vision. arXiv preprint arXiv:2006.03677"},{"key":"10824_CR29","unstructured":"Child R, Gray S, Radford A, Sutskever I (2019) Generating long sequences with sparse transformers. arXiv preprint arXiv:1904.10509"},{"key":"10824_CR30","unstructured":"Kitaev N, Kaiser \u0141, Levskaya A (2020) Reformer: the efficient transformer. arXiv preprint arXiv:2001.04451"},{"key":"10824_CR31","doi-asserted-by":"crossref","unstructured":"Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. 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, pp 78\u201388","DOI":"10.1007\/978-3-030-87193-2_8"},{"key":"10824_CR32","doi-asserted-by":"crossref","unstructured":"Cao H, Wang Y, Chen J, Jiang D, Zhang X, Tian Q, Wang M (2022) Swin-unet: Unet-like pure transformer for medical image segmentation. In: European conference on computer vision. Springer, pp 205\u2013218","DOI":"10.1007\/978-3-031-25066-8_9"},{"key":"10824_CR33","unstructured":"Qiu P, Yang J, Kumar S, Ghosh SS, Sotiras A (2024) AgileFormer: spatially agile transformer UNet for medical image segmentation. https:\/\/arxiv.org\/abs\/2404.00122"},{"key":"10824_CR34","doi-asserted-by":"crossref","unstructured":"Wang W, Chen C, Ding M, Yu H, Zha S, Li J (2021) Transbts: multimodal brain tumor segmentation using transformer. 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, pp 109\u2013119","DOI":"10.1007\/978-3-030-87193-2_11"},{"key":"10824_CR35","doi-asserted-by":"crossref","unstructured":"Xie Y, Zhang J, Shen C, Xia Y (2021) Cotr: efficiently bridging cnn and transformer for 3d medical image segmentation. In: Medical image computing and computer assisted intervention\u2013MICCAI 2021: 24th international conference, Strasbourg, France, September 27\u2013October 1, 2021, Proceedings, Part III 24. Springer, pp 171\u2013180","DOI":"10.1007\/978-3-030-87199-4_16"},{"key":"10824_CR36","doi-asserted-by":"crossref","unstructured":"Hatamizadeh A, Tang Y, Nath V, Yang D, Myronenko A, Landman B, Roth HR, Xu D (2022) Unetr: transformers for 3d medical image segmentation. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision, pp 574\u2013584","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"10824_CR37","doi-asserted-by":"crossref","unstructured":"Hatamizadeh A, Nath V, Tang Y, Yang D, Roth HR, Xu D (2021) Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion workshop. Springer, pp 272\u2013284","DOI":"10.1007\/978-3-031-08999-2_22"},{"key":"10824_CR38","unstructured":"Zhou H-Y, Guo J, Zhang Y, Yu L, Wang L, Yu Y (2021) nnformer: interleaved transformer for volumetric segmentation. arXiv preprint arXiv:2109.03201"},{"key":"10824_CR39","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2024.3398728","author":"AM Shaker","year":"2024","unstructured":"Shaker AM, Maaz M, Rasheed H, Khan S, Yang M-H, Khan FS (2024) Unetr++: delving into efficient and accurate 3d medical image segmentation. IEEE Trans Med Imaging. https:\/\/doi.org\/10.1109\/TMI.2024.3398728","journal-title":"IEEE Trans Med Imaging"},{"key":"10824_CR40","unstructured":"Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473"},{"issue":"2","key":"10824_CR41","first-page":"1578","volume":"2","author":"S Gupta","year":"2013","unstructured":"Gupta S, Mazumdar SG (2013) Sobel edge detection algorithm. Int J Comput Sci Manag Res 2(2):1578\u20131583","journal-title":"Int J Comput Sci Manag Res"},{"key":"10824_CR42","doi-asserted-by":"crossref","unstructured":"Milletari F, Navab N, Ahmadi S-A (2016) V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth international conference on 3D vision (3DV). IEEE, pp 565\u2013571","DOI":"10.1109\/3DV.2016.79"},{"key":"10824_CR43","doi-asserted-by":"publisher","unstructured":"Canny J (1987) A computational approach to edge detection. In: Fischler MA, Firschein O (eds) Readings in computer vision, pp 184\u2013203. https:\/\/doi.org\/10.1016\/B978-0-08-051581-6.50024-6","DOI":"10.1016\/B978-0-08-051581-6.50024-6"},{"issue":"11","key":"10824_CR44","doi-asserted-by":"publisher","first-page":"2514","DOI":"10.1109\/TMI.2018.2837502","volume":"37","author":"O Bernard","year":"2018","unstructured":"Bernard O, Lalande A, Zotti C, Cervenansky F, Yang X, Heng P-A, Cetin I, Lekadir K, Camara O, Ballester MAG et al (2018) Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Trans Med Imaging 37(11):2514\u20132525","journal-title":"IEEE Trans Med Imaging"},{"key":"10824_CR45","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-30695-9","author":"M Antonelli","year":"2022","unstructured":"Antonelli M, Reinke A, Bakas S, Farahani K, Kopp-Schneider A, Landman BA, Litjens G, Menze B, Ronneberger O, Summers RM, Ginneken B, Bilello M, Bilic P, Christ PF, Do RKG, Gollub MJ, Heckers SH, Huisman H, Jarnagin WR, McHugo MK, Napel S, Pernicka JSG, Rhode K, Tobon-Gomez C, Vorontsov E, Meakin JA, Ourselin S, Wiesenfarth M, Arbel\u00e1ez P, Bae B, Chen S, Daza L, Feng J, He B, Isensee F, Ji Y, Jia F, Kim I, Maier-Hein K, Merhof D, Pai A, Park B, Perslev M, Rezaiifar R, Rippel O, Sarasua I, Shen W, Son J, Wachinger C, Wang L, Wang Y, Xia Y, Xu D, Xu Z, Zheng Y, Simpson AL, Maier-Hein L, Cardoso MJ (2022) The medical segmentation decathlon. Nat Commun. https:\/\/doi.org\/10.1038\/s41467-022-30695-9","journal-title":"Nat Commun"},{"issue":"1","key":"10824_CR46","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1186\/s12880-015-0070-3","volume":"15","author":"AA Taha","year":"2015","unstructured":"Taha AA, Hanbury A (2015) Metrics for evaluating 3d medical image segmentation: analysis, selection, and tool. BMC Med Imaging 15(1):29. https:\/\/doi.org\/10.1186\/s12880-015-0070-3","journal-title":"BMC Med Imaging"},{"key":"10824_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2003.12.007","author":"KH Zou","year":"2004","unstructured":"Zou KH, Warfield SK, Bharatha A, Tempany CMC, Kaus MR, Haker SJ, Wells WM, Jolesz FA, Kikinis R (2004) Statistical validation of image segmentation quality based on a spatial overlap index: scientific reports. Acad Radiol. https:\/\/doi.org\/10.1016\/j.acra.2003.12.007","journal-title":"Acad Radiol"},{"key":"10824_CR48","unstructured":"Cardoso MJ, Li W, Brown R, Ma N, Kerfoot E, Wang Y, Murrey B, Myronenko A, Zhao C, Yang D, Nath V, He Y, Xu Z, Hatamizadeh A, Myronenko A, Zhu W, Liu Y, Zheng M, Tang Y, Yang I, Zephyr M, Hashemian B, Alle S, Darestani MZ, Budd C, Modat M, Vercauteren T, Wang G, Li Y, Hu Y, Fu Y, Gorman B, Johnson H, Genereaux B, Erdal BS, Gupta V, Diaz-Pinto A, Dourson A, Maier-Hein L, Jaeger PF, Baumgartner M, Kalpathy-Cramer J, Flores M, Kirby J, Cooper LAD, Roth HR, Xu D, Bericat D, Floca R, Zhou SK, Shuaib H, Farahani K, Maier-Hein KH, Aylward S, Dogra P, Ourselin S, Feng A (2022) MONAI: an open-source framework for deep learning in healthcare"},{"key":"10824_CR49","unstructured":"Cao H, Wang Y, Chen J, Jiang D, Zhang X, Tian Q, Wang M (2021) Swin-unet: Unet-like pure transformer for medical image segmentation"},{"key":"10824_CR50","unstructured":"Chen J, Lu Y, Yu Q, Luo X, Adeli E, Wang Y, Lu L, Yuille AL, Zhou Y (2021) TransUNet: transformers make strong encoders for medical image segmentation"},{"issue":"5","key":"10824_CR51","doi-asserted-by":"publisher","first-page":"1484","DOI":"10.1109\/TMI.2022.3230943","volume":"42","author":"X Huang","year":"2022","unstructured":"Huang X, Deng Z, Li D, Yuan X, Fu Y (2022) Missformer: an effective transformer for 2d medical image segmentation. IEEE Trans Med Imaging 42(5):1484\u20131494","journal-title":"IEEE Trans Med Imaging"},{"key":"10824_CR52","doi-asserted-by":"crossref","unstructured":"Roy S, Koehler G, Ulrich C, Baumgartner M, Petersen J, Isensee F, Jaeger PF, Maier-Hein K (2024) MedNeXt: transformer-driven scaling of ConvNets for medical image segmentation. https:\/\/arxiv.org\/abs\/2303.09975","DOI":"10.1007\/978-3-031-43901-8_39"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10824-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-10824-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10824-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,6]],"date-time":"2025-03-06T13:57:52Z","timestamp":1741269472000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-10824-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,7]]},"references-count":52,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["10824"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-10824-9","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2025,1,7]]},"assertion":[{"value":"1 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 January 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All the authors declare that there are no interest conflicts that could influence the work presented in this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The Adenoid Hypertrophy Segmentation Dataset (AHSD) collected from Shenzhen University General Hospital in this study underwent strict ethical review during the data collection process and was officially approved by the Ethics and Welfare Committee of Shenzhen University General Hospital (Approval Number: KYLL-20230402A). All content in the experiment has obtained informed consent from the patient.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical and informed consent for data used"}}]}}