{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T03:53:46Z","timestamp":1781495626172,"version":"3.54.1"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T00:00:00Z","timestamp":1779321600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T00:00:00Z","timestamp":1779321600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100021171","name":"Basic and Applied Basic Research Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2022A1515011549"],"award-info":[{"award-number":["2022A1515011549"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1007\/s11517-026-03591-3","type":"journal-article","created":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T03:50:43Z","timestamp":1779335443000},"page":"2393-2404","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Uncertainty-guided model learning for trustworthy medical image segmentation"],"prefix":"10.1007","volume":"64","author":[{"given":"Qi","family":"Ye","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4916-3930","authenticated-orcid":false,"given":"Lihua","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,21]]},"reference":[{"key":"3591_CR1","doi-asserted-by":"publisher","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015, pp 234\u2013241. https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"2","key":"3591_CR2","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. https:\/\/doi.org\/10.1038\/s41592-020-01008-z","journal-title":"Nat Methods"},{"key":"3591_CR3","doi-asserted-by":"publisher","unstructured":"Oktay O, Schlemper J, Folgoc LL, Lee M, Heinrich M, Misawa K, Mori K, McDonagh S, Hammerla NY, Kainz B et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999. https:\/\/doi.org\/10.48550\/arxiv.1804.03999","DOI":"10.48550\/arxiv.1804.03999"},{"issue":"10","key":"3591_CR4","doi-asserted-by":"publisher","first-page":"1119","DOI":"10.3390\/bioengineering10101119","volume":"10","author":"Z Huang","year":"2023","unstructured":"Huang Z, Yang X, Huang S, Guo L (2023) Secp-net: Se-connection pyramid network for segmentation of organs at risk with nasopharyngeal carcinoma. Bioengineering 10(10):1119. https:\/\/doi.org\/10.3390\/bioengineering10101119","journal-title":"Bioengineering"},{"key":"3591_CR5","doi-asserted-by":"publisher","first-page":"102680","DOI":"10.1016\/j.media.2022.102680","volume":"84","author":"P Bilic","year":"2023","unstructured":"Bilic P, Christ P, Li HB, Vorontsov E, Ben-Cohen A, Kaissis G, Szeskin A, Jacobs C, Mamani GEH, Chartrand G et al (2023) The liver tumor segmentation benchmark (lits). Med Image Anal 84:102680. https:\/\/doi.org\/10.1016\/j.media.2022.102680","journal-title":"Med Image Anal"},{"issue":"11","key":"3591_CR6","doi-asserted-by":"publisher","first-page":"815","DOI":"10.1016\/s2589-7500(24)00154-7","volume":"6","author":"J Ma","year":"2024","unstructured":"Ma J, Zhang Y, Gu S, Ge C, Mae S, Young A, Zhu C, Yang X, Meng K, Huang Z et al (2024) Unleashing the strengths of unlabelled data in deep learning-assisted pan-cancer abdominal organ quantification: the flare22 challenge. Lancet Digit Health 6(11):815\u2013826. https:\/\/doi.org\/10.1016\/s2589-7500(24)00154-7","journal-title":"Lancet Digit Health"},{"issue":"2","key":"3591_CR7","doi-asserted-by":"publisher","first-page":"2551","DOI":"10.1109\/TPAMI.2022.3171983","volume":"45","author":"Z Han","year":"2023","unstructured":"Han Z, Zhang C, Fu H, Zhou JT (2023) Trusted multi-view classification with dynamic evidential fusion. IEEE Trans Pattern Anal Mach Intell 45(2):2551\u20132566. https:\/\/doi.org\/10.1109\/TPAMI.2022.3171983","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3591_CR8","unstructured":"Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. In: Advances in neural information processing systems, vol 30"},{"issue":"1","key":"3591_CR9","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1038\/s42256-018-0004-1","volume":"1","author":"E Begoli","year":"2019","unstructured":"Begoli E, Bhattacharya T, Kusnezov D (2019) The need for uncertainty quantification in machine-assisted medical decision making. Nat Mach Intell 1(1):20\u201323. https:\/\/doi.org\/10.1038\/s42256-018-0004-1","journal-title":"Nat Mach Intell"},{"issue":"12","key":"3591_CR10","doi-asserted-by":"publisher","first-page":"229101","DOI":"10.1007\/s11432-020-3162-4","volume":"65","author":"L Guo","year":"2022","unstructured":"Guo L, Chen D, Jia K (2022) Knowledge transferred adaptive filter pruning for cnn compression and acceleration. Sci China Inf Sci 65(12):229101. https:\/\/doi.org\/10.1007\/s11432-020-3162-4","journal-title":"Sci China Inf Sci"},{"issue":"8","key":"3591_CR11","doi-asserted-by":"publisher","first-page":"669","DOI":"10.1038\/s42256-022-00516-1","volume":"4","author":"W Liang","year":"2022","unstructured":"Liang W, Tadesse GA, Ho D, Fei-Fei L, Zaharia M, Zhang C, Zou J (2022) Advances, challenges and opportunities in creating data for trustworthy ai. Nat Mach Intell 4(8):669\u2013677. https:\/\/doi.org\/10.1038\/s42256-022-00516-1","journal-title":"Nat Mach Intell"},{"key":"3591_CR12","unstructured":"Lee K, Lee K, Lee H, Shin J (2018) A simple unified framework for detecting out-of-distribution samples and adversarial attacks. In: Advances in neural information processing systems, vol 31"},{"key":"3591_CR13","unstructured":"Van Amersfoort J, Smith L, Teh YW, Gal Y (2020) Uncertainty estimation using a single deep deterministic neural network. In: International conference on machine learning, pp 9690\u20139700"},{"key":"3591_CR14","unstructured":"Kendall A, Gal Y (2017) What uncertainties do we need in bayesian deep learning for computer vision? In: Advances in neural information processing systems, vol 30"},{"key":"3591_CR15","doi-asserted-by":"publisher","unstructured":"Der Kiureghian A, Ditlevsen O (2009) Aleatory or epistemic? does it matter? Structural safety, 105\u2013112. https:\/\/doi.org\/10.1016\/j.strusafe.2008.06.020","DOI":"10.1016\/j.strusafe.2008.06.020"},{"issue":"1","key":"3591_CR16","doi-asserted-by":"publisher","first-page":"100003","DOI":"10.1016\/j.metrad.2023.100003","volume":"1","author":"K Zou","year":"2023","unstructured":"Zou K, Chen Z, Yuan X, Shen X, Wang M, Fu H (2023) A review of uncertainty estimation and its application in medical imaging. Meta-Radiol 1(1):100003. https:\/\/doi.org\/10.1016\/j.metrad.2023.100003","journal-title":"Meta-Radiol"},{"issue":"3","key":"3591_CR17","doi-asserted-by":"publisher","first-page":"035007","DOI":"10.1088\/1361-6560\/ad1a26","volume":"69","author":"FC Maruccio","year":"2024","unstructured":"Maruccio FC, Eppinga W, Laves M-H, Navarro RF, Salvi M, Molinari F, Papaconstadopoulos P (2024) Clinical assessment of deep learning-based uncertainty maps in lung cancer segmentation. Phys Med Biol 69(3):035007. https:\/\/doi.org\/10.1088\/1361-6560\/ad1a26","journal-title":"Phys Med Biol"},{"key":"3591_CR18","doi-asserted-by":"publisher","first-page":"103902","DOI":"10.1016\/j.media.2025.103902","volume":"109","author":"X Ma","year":"2026","unstructured":"Ma X, Tao Y, Zhang Z, Zhang Y, Wang X, Zhang S, Ji Z, Zhang Y, Chen Q, Yang G (2026) Test-time generative augmentation for medical image segmentation. Med Image Anal 109:103902. https:\/\/doi.org\/10.1016\/j.media.2025.103902","journal-title":"Med Image Anal"},{"key":"3591_CR19","doi-asserted-by":"publisher","unstructured":"Mukhoti J, Kirsch A, Amersfoort J, Torr PHS, Gal Y (2023) Deep deterministic uncertainty: A new simple baseline. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 24384\u201324394. https:\/\/doi.org\/10.1109\/cvpr52729.2023.02336","DOI":"10.1109\/cvpr52729.2023.02336"},{"key":"3591_CR20","unstructured":"Gal Y, Ghahramani Z (2016) Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: International conference on machine learning, pp 1050\u20131059"},{"key":"3591_CR21","unstructured":"Sensoy M, Kaplan L, Kandemir M (2018) Evidential deep learning to quantify classification uncertainty. In: Advances in neural information processing systems, vol 31, pp 3183\u20133193"},{"key":"3591_CR22","doi-asserted-by":"publisher","unstructured":"Zou K, Yuan X, Shen X, Wang M, Fu H (2022) Tbrats: Trusted brain tumor segmentation. Intern Conf Med Image Comput Computer-Assist Intervent 503\u2013513. https:\/\/doi.org\/10.1007\/978-3-031-16452-1_48","DOI":"10.1007\/978-3-031-16452-1_48"},{"issue":"1","key":"3591_CR23","doi-asserted-by":"publisher","first-page":"6757","DOI":"10.1038\/s41467-023-42444-7","volume":"14","author":"M Wang","year":"2023","unstructured":"Wang M, Lin T, Wang L, Lin A, Zou K, Xu X, Zhou Y, Peng Y, Meng Q, Qian Y et al (2023) Uncertainty-inspired open set learning for retinal anomaly identification. Nat Commun 14(1):6757. https:\/\/doi.org\/10.1038\/s41467-023-42444-7","journal-title":"Nat Commun"},{"issue":"12","key":"3591_CR24","doi-asserted-by":"publisher","first-page":"5975","DOI":"10.1109\/TCYB.2025.3604432","volume":"55","author":"K Zou","year":"2025","unstructured":"Zou K, Chen Y, Huang L, Zhou N, Yuan X, Shen X, Wang M, Goh RSM, Liu Y, Tham YC, Fu H (2025) Toward reliable medical image segmentation by modeling evidential calibrated uncertainty. IEEE Trans Cybern 55(12):5975\u20135988. https:\/\/doi.org\/10.1109\/TCYB.2025.3604432","journal-title":"IEEE Trans Cybern"},{"key":"3591_CR25","doi-asserted-by":"publisher","unstructured":"Jsang A (2018) Subjective logic: A formalism for reasoning under uncertainty. Springer Publishing Company, Incorporated, 8029\u20138036. https:\/\/doi.org\/10.5555\/3279217","DOI":"10.5555\/3279217"},{"key":"3591_CR26","unstructured":"Kohl S, Romera-Paredes B, Meyer C, De Fauw J, Ledsam JR, Maier-Hein K, Eslami S, Jimenez Rezende D, Ronneberger O (2018) A probabilistic u-net for segmentation of ambiguous images. In: Advances in neural information processing systems, vol 31"},{"key":"3591_CR27","doi-asserted-by":"publisher","unstructured":"Zeevi T, Staib LH, Onofrey JA (2025) Enhancing uncertainty estimation in semantic segmentation via monte-carlo frequency dropout. In: 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI), pp 1\u20135. https:\/\/doi.org\/10.1109\/ISBI60581.2025.10980684","DOI":"10.1109\/ISBI60581.2025.10980684"},{"key":"3591_CR28","doi-asserted-by":"publisher","first-page":"1048","DOI":"10.59275\/j.melba.2024-5gc8","volume":"2","author":"P Mody","year":"2024","unstructured":"Mody P, Chaves-de-Plaza NF, Rao C, Astrenidou E, Ridder M, Hoekstra N, Hildebrandt K, Staring M (2024) Improving uncertainty-error correspondence in deep bayesian medical image segmentation. Mach Learn Biomed Imag 2:1048\u20131082. https:\/\/doi.org\/10.59275\/j.melba.2024-5gc8","journal-title":"Mach Learn Biomed Imag"},{"issue":"12","key":"3591_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, Abolmaesumi P, Kapur T (2020) Confidence calibration and predictive uncertainty estimation for deep medical image segmentation. IEEE Trans Med Imaging 39(12):3868\u20133878. https:\/\/doi.org\/10.1109\/TMI.2020.3006437","journal-title":"IEEE Trans Med Imaging"},{"key":"3591_CR30","doi-asserted-by":"publisher","first-page":"110258","DOI":"10.1016\/j.compbiomed.2025.110258","volume":"194","author":"W Chen","year":"2025","unstructured":"Chen W, McMillan AB (2025) Saswise-ue: Segmentation and synthesis with interpretable scalable ensembles for uncertainty estimation. Comput Biol Med 194:110258. https:\/\/doi.org\/10.1016\/j.compbiomed.2025.110258","journal-title":"Comput Biol Med"},{"key":"3591_CR31","doi-asserted-by":"publisher","unstructured":"Huang L, Ruan S, Den\u0153ux T (2021) Belief function-based semi-supervised learning for brain tumor segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp 160\u2013164. https:\/\/doi.org\/10.1109\/ISBI48211.2021.9433885","DOI":"10.1109\/ISBI48211.2021.9433885"},{"key":"3591_CR32","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1111\/j.2517-6161.1968.tb00722.x","volume":"219","author":"AP Dempster","year":"2008","unstructured":"Dempster AP (2008) A generalization of bayesian inference. Classic Works Dempster-Shafer Theor Belief Funct 219:73\u2013104. https:\/\/doi.org\/10.1111\/j.2517-6161.1968.tb00722.x","journal-title":"Classic Works Dempster-Shafer Theor Belief Funct"}],"container-title":["Medical & Biological Engineering & Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-026-03591-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-026-03591-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-026-03591-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T03:18:17Z","timestamp":1781493497000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-026-03591-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,21]]},"references-count":32,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["3591"],"URL":"https:\/\/doi.org\/10.1007\/s11517-026-03591-3","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,21]]},"assertion":[{"value":"23 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 May 2026","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 that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}