{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T19:49:04Z","timestamp":1771703344237,"version":"3.50.1"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2023,9,4]],"date-time":"2023-09-04T00:00:00Z","timestamp":1693785600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,9,4]],"date-time":"2023-09-04T00:00:00Z","timestamp":1693785600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["Grant No. 62002327"],"award-info":[{"award-number":["Grant No. 62002327"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U22A2040"],"award-info":[{"award-number":["U22A2040"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["Grant No. 61976190"],"award-info":[{"award-number":["Grant No. 61976190"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["Grant No. 62073294"],"award-info":[{"award-number":["Grant No. 62073294"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["Grant No. LZ21F030003"],"award-info":[{"award-number":["Grant No. LZ21F030003"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["Grant No. LQ21F020017"],"award-info":[{"award-number":["Grant No. LQ21F020017"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]},{"name":"The Key Technology Research and Development Program of Zhejiang Province","award":["Grant No.2020C03070"],"award-info":[{"award-number":["Grant No.2020C03070"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s11517-023-02899-8","type":"journal-article","created":{"date-parts":[[2023,9,4]],"date-time":"2023-09-04T09:02:38Z","timestamp":1693818158000},"page":"3289-3301","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Uncertainty-guided transformer for brain tumor segmentation"],"prefix":"10.1007","volume":"61","author":[{"given":"Zan","family":"Chen","sequence":"first","affiliation":[]},{"given":"Chenxu","family":"Peng","sequence":"additional","affiliation":[]},{"given":"Wenlong","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Xie","sequence":"additional","affiliation":[]},{"given":"Shanshan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Qichuan","family":"Zhuge","sequence":"additional","affiliation":[]},{"given":"Caiyun","family":"Wen","sequence":"additional","affiliation":[]},{"given":"Yuanjing","family":"Feng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,4]]},"reference":[{"issue":"11","key":"2899_CR1","doi-asserted-by":"publisher","first-page":"6497","DOI":"10.1245\/s10434-022-12151-6","volume":"29","author":"S Deo","year":"2022","unstructured":"Deo S, Sharma J, Kumar S (2022) Globocan 2020 report on global cancer burden: challenges and opportunities for surgical oncologists. Ann Surg Oncol 29(11):6497\u20136500","journal-title":"Ann Surg Oncol"},{"key":"2899_CR2","unstructured":"Farmanfarma K. K., M, Mohammadian Shahabinia Z, et al. (2019) \u201cBrain cancer in the world: an epidemiological review,\u201d World Cancer Research Journal 6(5),"},{"key":"2899_CR3","doi-asserted-by":"crossref","unstructured":"Hoover J.M, Morris J.M, and Meyer F.B, (2011) \u201cUse of preoperative magnetic resonance imaging t1 and t2 sequences to determine intraoperative meningioma consistency,\u201d Surg Neurol Int 2","DOI":"10.4103\/2152-7806.85983"},{"key":"2899_CR4","doi-asserted-by":"crossref","unstructured":"Zhang D, Huang G, Zhang Q et al (2021) Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110 107562","DOI":"10.1016\/j.patcog.2020.107562"},{"key":"2899_CR5","doi-asserted-by":"crossref","unstructured":"Zhang Y, Yang J, Tian J, et al. (2021) \u201cModality-aware mutual learning for multi-modal medical image segmentation,\u201d International Conference on Medical Image Computing and Computer-Assisted Intervention, 589\u2013599","DOI":"10.1007\/978-3-030-87193-2_56"},{"key":"2899_CR6","doi-asserted-by":"publisher","first-page":"123649","DOI":"10.1109\/ACCESS.2020.3005687","volume":"8","author":"B Jin","year":"2020","unstructured":"Jin B, Cruz L, Gon\u00e7alves N (2020) Deep facial diagnosis: deep transfer learning from face recognition to facial diagnosis. IEEE Access 8:123649\u2013123661","journal-title":"IEEE Access"},{"issue":"13","key":"2899_CR7","doi-asserted-by":"publisher","first-page":"7723","DOI":"10.1007\/s00521-020-05514-1","volume":"33","author":"Q Zheng","year":"2021","unstructured":"Zheng Q, Zhao P, Li Y et al (2021) Spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification. Neural Comput Appl 33(13):7723-7745","journal-title":"Neural Comput Appl"},{"issue":"12","key":"2899_CR8","doi-asserted-by":"publisher","first-page":"7204","DOI":"10.1002\/int.22586","volume":"36","author":"Q Zheng","year":"2021","unstructured":"Zheng Q, Zhao P, Zhang D et al (2021) Mr-dcae: manifold regularization-based deep convolutional autoencoder for unauthorized broadcasting identification. Int J Intell Syst 36(12):7204\u20137238","journal-title":"Int J Intell Syst"},{"key":"2899_CR9","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.ins.2021.04.053","volume":"571","author":"Z Xiao","year":"2021","unstructured":"Xiao Z, Xu X, Xing H et al (2021) Rtfn: a robust temporal feature network for time series classification. Information sciences 571:65\u201386","journal-title":"Information sciences"},{"issue":"3","key":"2899_CR10","doi-asserted-by":"publisher","first-page":"793","DOI":"10.1007\/s11045-019-00686-z","volume":"31","author":"Q Zheng","year":"2020","unstructured":"Zheng Q, Tian X, Yang M et al (2020) Pac-bayesian framework based drop-path method for 2d discriminative convolutional network pruning. Multidimens Syst Signal 31(3):793\u2013827","journal-title":"Multidimens Syst Signal"},{"key":"2899_CR11","doi-asserted-by":"crossref","unstructured":"Jiang Z, Ding C, Liu M et al (2019) Two-stage cascaded u-net: 1st place solution to brats challenge 2019 segmentation task in International MICCAI brainlesion workshop, 231\u2013241. Springer","DOI":"10.1007\/978-3-030-46640-4_22"},{"key":"2899_CR12","doi-asserted-by":"crossref","unstructured":"Isensee F, J\u00e4ger P.F., Full P.M., et al. (2020) \u201cnnu-net for brain tumor segmentation,\u201d International MICCAI Brainlesion Workshop, 118\u2013132","DOI":"10.1007\/978-3-030-72087-2_11"},{"issue":"2","key":"2899_CR13","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 et al (2021) nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods 18(2):203\u2013211","journal-title":"Nature methods"},{"key":"2899_CR14","doi-asserted-by":"crossref","unstructured":"Wang W, Chen C, Ding M, et al. (2021) \u201cTransbts: multimodal brain tumor segmentation using transformer,\u201d in International Conference on Medical Image Computing and Computer-Assisted Intervention, 109\u2013119","DOI":"10.1007\/978-3-030-87193-2_11"},{"key":"2899_CR15","doi-asserted-by":"crossref","unstructured":"Hatamizadeh A, Tang Y, Nath V, et al. (2022) \u201cUnetr: transformers for 3d medical image segmentation,\u201d in Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, 574\u2013584","DOI":"10.1109\/WACV51458.2022.00181"},{"issue":"6","key":"2899_CR16","doi-asserted-by":"publisher","first-page":"1298","DOI":"10.1109\/LCOMM.2022.3145647","volume":"26","author":"Q Zheng","year":"2022","unstructured":"Zheng Q, Zhao P, Wang H et al (2022) Fine-grained modulation classification using multi-scale radio transformer with dual-channel representation. IEEE Communications Letters 26(6):1298\u20131302","journal-title":"IEEE Communications Letters"},{"key":"2899_CR17","unstructured":"Touvron H, Cord M, Douze M, et al. (2021) \u201cTraining data-efficient image transformers & distillation through attention,\u201d in International Conference on Machine Learning, 10347\u201310357"},{"key":"2899_CR18","doi-asserted-by":"crossref","unstructured":"Zheng S, Lu J, Zhao H, et al. (2021) \u201cRethinking semantic segmentation from a sequence-to-sequence perspective with transformers,\u201d in Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, 6881\u20136890","DOI":"10.1109\/CVPR46437.2021.00681"},{"key":"2899_CR19","doi-asserted-by":"crossref","unstructured":"Chen J, Lu Y, Yu Q, et\u00a0al. (2021) \u201cTransunet: transformers make strong encoders for medical image segmentation,\u201d arXiv:2102.04306","DOI":"10.1109\/IGARSS46834.2022.9883628"},{"key":"2899_CR20","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y, et al. (2021) \u201cSwin transformer: hierarchical vision transformer using shifted windows,\u201d in Proceedings of the IEEE\/CVF International Conference o Computer Vision, 10012\u201310022","DOI":"10.1109\/ICCV48922.2021.00986"},{"issue":"1","key":"2899_CR21","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1109\/TMI.2015.2463078","volume":"35","author":"M Goetz","year":"2015","unstructured":"Goetz M, Weber C, Binczyk F et al (2015) Dalsa: domain adaptation for supervised learning from sparsely annotated mr images. IEEE transactions on medical imaging 35(1):184\u2013196","journal-title":"IEEE transactions on medical imaging"},{"key":"2899_CR22","volume-title":"Ensembles of densely-connected cnns with label-uncertainty for brain tumor segmentation, in International MICCAI Brainlesion Workshop, 456\u2013465","author":"R McKinley","year":"2018","unstructured":"McKinley R, Meier R, Wiest R (2018) Ensembles of densely-connected cnns with label-uncertainty for brain tumor segmentation, in International MICCAI Brainlesion Workshop, 456\u2013465. Springer"},{"key":"2899_CR23","doi-asserted-by":"crossref","unstructured":"Jungo A, McKinley R, Meier R et al (2017) Towards uncertainty-assisted brain tumor segmentation and survival prediction, in International MICCAI Brainlesion Workshop, 474\u2013485. Springer","DOI":"10.1007\/978-3-319-75238-9_40"},{"key":"2899_CR24","unstructured":"Lakshminarayanan B, Pritzel A, and Blundell C, (2017) \u201cSimple and scalable predictive uncertainty estimation using deep ensembles,\u201d Advances in neural information processing systems 30"},{"key":"2899_CR25","unstructured":"Gal Y, and Ghahramani Z, (2016) \u201cDropout as a bayesian approximation: Representing model uncertainty in deep learning,\u201d in international conference on machine learning, 1050\u20131059, PMLR"},{"key":"2899_CR26","unstructured":"Amersfoort J. Van, Smith L, Teh Y. W, et al. (2020) \u201cUncertainty estimation using a single deep deterministic neural network,\u201d in International conference on machine learning, 9690\u20139700, PMLR"},{"key":"2899_CR27","unstructured":"Sensoy M, Kaplan L, and Kandemir M, (2018) \u201cEvidential deep learning to quantify classification uncertainty,\u201d Advances in neural information processing systems 31"},{"key":"2899_CR28","doi-asserted-by":"crossref","unstructured":"McKinley R, Rebsamen M, Meier R et al (2020) Triplanar ensemble of 3d-to-2d cnns with label-uncertainty for brain tumor segmentation, in Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 5th International Workshop, BrainLes 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Revised Selected Papers, Part I 5, 379\u2013387. Springer","DOI":"10.1007\/978-3-030-46640-4_36"},{"issue":"12","key":"2899_CR29","doi-asserted-by":"publisher","first-page":"3868","DOI":"10.1109\/TMI.2020.3006437","volume":"39","author":"A Mehrtash","year":"2020","unstructured":"Mehrtash A, Wells WM, Tempany CM et al (2020) Confidence calibration and predictive uncertainty estimation for deep medical image segmentation. IEEE transactions on medical imaging 39(12):3868\u20133878","journal-title":"IEEE transactions on medical imaging"},{"key":"2899_CR30","doi-asserted-by":"crossref","unstructured":"Jungo A, Meier R, Ermis E et al (2018) On the effect of inter-observer variability for a reliable estimation of uncertainty of medical image segmentation, in Medical Image Computing and Computer Assisted Intervention-MICCAI 2018: 21st International Conference, Granada, Spain, September 16\u201320, 2018, Proceedings, Part I, 682\u2013690. Springer","DOI":"10.1007\/978-3-030-00928-1_77"},{"key":"2899_CR31","doi-asserted-by":"crossref","unstructured":"Nair T, Precup D, Arnold DL et al (2020) Exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation. Medical image analysis 59 101557","DOI":"10.1016\/j.media.2019.101557"},{"key":"2899_CR32","unstructured":"Kohl S, Romera-Paredes B, Meyer C, et al. (2018) \u201cA probabilistic u-net for segmentation of ambiguous images,\u201d Adv Neural Inf Process 31"},{"key":"2899_CR33","unstructured":"Mukhoti J, Amersfoort J, van Torr P.H., et al. (2021) \u201cDeep deterministic uncertainty for semantic segmentation,\u201d arXiv:2111.00079"},{"key":"2899_CR34","doi-asserted-by":"crossref","unstructured":"Peiris H, Hayat M, Chen Z, et al. (2021) \u201cA volumetric transformer for accurate 3d tumor segmentation,\u201d arXiv:2111.13300","DOI":"10.1007\/978-3-031-16443-9_16"},{"key":"2899_CR35","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, et al. (2020) \u201cAn image is worth 16x16 words: transformers for image recognition at scale,\u201d arXiv:2010.11929"},{"key":"2899_CR36","doi-asserted-by":"crossref","unstructured":"Yang F, Zhai Q, Li X, et al. (2021) \u201cUncertainty-guided transformer reasoning for camouflaged object detection,\u201d in Proceedings of the IEEE\/CVF International Conference on Computer Vision, 4146\u20134155","DOI":"10.1109\/ICCV48922.2021.00411"},{"key":"2899_CR37","doi-asserted-by":"crossref","unstructured":"Myronenko A (2018) \u201c3d mri brain tumor segmentation using autoencoder regularization,\u201d in International MICCAI Brainlesion Workshop, 311\u2013320","DOI":"10.1007\/978-3-030-11726-9_28"},{"issue":"1","key":"2899_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12880-015-0068-x","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 medical imaging 15(1):1\u201328","journal-title":"BMC medical imaging"},{"issue":"10","key":"2899_CR39","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2014","unstructured":"Menze BH, Jakab A, Bauer S et al (2014) The multimodal brain tumor image segmentation benchmark (brats). IEEE transactions on medical imaging 34(10):1993\u20132024","journal-title":"IEEE transactions on medical imaging"},{"key":"2899_CR40","doi-asserted-by":"crossref","unstructured":"Chang J, Zhang X, Ye M, et al. (2018) \u201cBrain tumor segmentation based on 3d unet with multi-class focal loss,\u201d in 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, 1\u20135","DOI":"10.1109\/CISP-BMEI.2018.8633056"},{"key":"2899_CR41","doi-asserted-by":"crossref","unstructured":"Chen Z, Xie L, Chen Y et al. (2021) \u201cGenerative adversarial network based cerebrovascular segmentation for time-of-flight magnetic resonance angiography image,\u201d Neurocomputing","DOI":"10.1016\/j.neucom.2021.11.075"},{"key":"2899_CR42","doi-asserted-by":"crossref","unstructured":"Chen C, Liu X, Ding M, et al. (2019) \u201c3d dilated multi-fiber network for real-time brain tumor segmentation in mri,\u201d in International Conference on Medical Image Computing and Computer-Assisted Intervention, 184\u2013192","DOI":"10.1007\/978-3-030-32248-9_21"},{"key":"2899_CR43","doi-asserted-by":"crossref","unstructured":"Shen H, Wang R, Zhang J, et al. (2017) \u201cBoundary-aware fully convolutional network for brain tumor segmentation,\u201d in International Conference on Medical Image Computing and Computer-Assisted Intervention, 433\u2013441, Springer","DOI":"10.1007\/978-3-319-66185-8_49"},{"key":"2899_CR44","doi-asserted-by":"crossref","unstructured":"Xiao Z, Zhang H, Tong H, et al. (2022) \u201cAn efficient temporal network with dual self-distillation for electroencephalography signal classification,\u201d in 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 1759\u20131762, IEEE","DOI":"10.1109\/BIBM55620.2022.9995049"},{"issue":"11","key":"2899_CR45","doi-asserted-by":"publisher","first-page":"8583","DOI":"10.1002\/int.22957","volume":"37","author":"H Xing","year":"2022","unstructured":"Xing H, Xiao Z, Zhan D et al (2022) Selfmatch: robust semisupervised time-series classification with self-distillation. Int J Intell Syst 37(11):8583\u20138610","journal-title":"Int J Intell Syst"},{"issue":"6","key":"2899_CR46","doi-asserted-by":"publisher","first-page":"1520","DOI":"10.1109\/TMI.2022.3142321","volume":"41","author":"J Cheng","year":"2022","unstructured":"Cheng J, Liu J, Kuang H et al (2022) A fully automated multimodal mri-based multi-task learning for glioma segmentation and idh genotyping. IEEE Transactions on Medical Imaging 41(6):1520\u20131532","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"2899_CR47","doi-asserted-by":"crossref","unstructured":"Tanno R, Saeedi A, Sankaranarayanan S et al. (2019) \u201cLearning from noisy labels by regularized estimation of annotator confusion,\u201d in Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, 11244\u201311253","DOI":"10.1109\/CVPR.2019.01150"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-023-02899-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-023-02899-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-023-02899-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,23]],"date-time":"2023-12-23T03:14:41Z","timestamp":1703301281000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-023-02899-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,4]]},"references-count":47,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["2899"],"URL":"https:\/\/doi.org\/10.1007\/s11517-023-02899-8","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,4]]},"assertion":[{"value":"4 January 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 June 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 September 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}