{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,4]],"date-time":"2025-05-04T04:02:34Z","timestamp":1746331354509,"version":"3.40.4"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T00:00:00Z","timestamp":1742256000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T00:00:00Z","timestamp":1742256000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100004479","name":"Natural Science Foundation of Jiangxi Province","doi-asserted-by":"publisher","award":["20224BAB212013,20224BAB212008,20224BAB202002","20224BAB212013,20224BAB212008,20224BAB202002","20224BAB212013,20224BAB212008,20224BAB202002"],"award-info":[{"award-number":["20224BAB212013,20224BAB212008,20224BAB202002","20224BAB212013,20224BAB212008,20224BAB202002","20224BAB212013,20224BAB212008,20224BAB202002"]}],"id":[{"id":"10.13039\/501100004479","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62266020,62261027","62266020,62261027"],"award-info":[{"award-number":["62266020,62261027","62266020,62261027"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Real-Time Image Proc"],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1007\/s11554-025-01648-4","type":"journal-article","created":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T17:57:28Z","timestamp":1742320648000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dual-branch evidential framework fusing hard example mining for abdominal organ segmentation"],"prefix":"10.1007","volume":"22","author":[{"given":"Xiangchun","family":"Yu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianqi","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dingwen","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Miaomiao","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingjuan","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,18]]},"reference":[{"key":"1648_CR1","doi-asserted-by":"crossref","unstructured":"Ahn, S., Hu, S.X., Damianou, A., Lawrence, N.D., Dai, Z.: Variational information distillation for knowledge transfer. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9163\u20139171 (2019)","DOI":"10.1109\/CVPR.2019.00938"},{"issue":"1","key":"1648_CR2","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.: The need for uncertainty quantification in machine-assisted medical decision making. Nat. Mach. Intell. 1(1), 20\u201323 (2019)","journal-title":"Nat. Mach. Intell."},{"key":"1648_CR3","unstructured":"Cardoso, M.J., Li, W., Brown, R., Ma, N., Kerfoot, E., Wang, Y., Murrey, B., Myronenko, A., Zhao, C., Yang, D., et\u00a0al.: MONAI: an open-source framework for deep learning in healthcare. arXiv preprint arXiv:2211.02701 (2022)"},{"key":"1648_CR4","unstructured":"Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: TransUNET: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)"},{"issue":"2","key":"1648_CR5","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1111\/j.2517-6161.1968.tb00722.x","volume":"30","author":"AP Dempster","year":"1968","unstructured":"Dempster, A.P.: A generalization of Bayesian inference. J. R. Stat. Soc. Ser. B (Methodol.) 30(2), 205\u2013232 (1968)","journal-title":"J. R. Stat. Soc. Ser. B (Methodol.)"},{"key":"1648_CR6","unstructured":"Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In: international Conference on Machine Learning, pp. 1050\u20131059. PMLR (2016)"},{"issue":"Suppl 1","key":"1648_CR7","doi-asserted-by":"publisher","first-page":"1513","DOI":"10.1007\/s10462-023-10562-9","volume":"56","author":"J Gawlikowski","year":"2023","unstructured":"Gawlikowski, J., Tassi, C.R.N., Ali, M., Lee, J., Humt, M., Feng, J., Kruspe, A., Triebel, R., Jung, P., Roscher, R., et al.: A survey of uncertainty in deep neural networks. Artif. Intell. Rev. 56(Suppl 1), 1513\u20131589 (2023)","journal-title":"Artif. Intell. Rev."},{"issue":"11","key":"1648_CR8","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial networks. Commun. ACM 63(11), 139\u2013144 (2020)","journal-title":"Commun. ACM"},{"key":"1648_CR9","unstructured":"Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)"},{"key":"1648_CR10","unstructured":"Guo, C., Pleiss, G., Sun, Y., Weinberger, K.Q.: On calibration of modern neural networks. In: International Conference on Machine Learning, pp. 1321\u20131330. PMLR (2017)"},{"key":"1648_CR11","unstructured":"Han, Z., Zhang, C., Fu, H., Zhou, J.T.: Trusted multi-view classification. In: International Conference on Learning Representations (2020)"},{"issue":"2","key":"1648_CR12","doi-asserted-by":"publisher","first-page":"2551","DOI":"10.1109\/TPAMI.2022.3171983","volume":"45","author":"Z Han","year":"2022","unstructured":"Han, Z., Zhang, C., Fu, H., Zhou, J.T.: Trusted multi-view classification with dynamic evidential fusion. IEEE Trans. Pattern Anal. Mach. Intell. 45(2), 2551\u20132566 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1648_CR13","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part IV 14, pp. 630\u2013645. Springer (2016)","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"1648_CR14","unstructured":"Hern\u00e1ndez-Lobato, J.M., Adams, R.: Probabilistic backpropagation for scalable learning of Bayesian neural networks. In: International Conference on Machine Learning, pp. 1861\u20131869. PMLR (2015)"},{"key":"1648_CR15","doi-asserted-by":"crossref","unstructured":"Hinton, G.E., Van\u00a0Camp, D.: Keeping the neural networks simple by minimizing the description length of the weights. In: Proceedings of the Sixth Annual Conference on Computational Learning Theory, pp. 5\u201313 (1993)","DOI":"10.1145\/168304.168306"},{"key":"1648_CR16","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.: MISSFormer: an effective transformer for 2D medical image segmentation. IEEE Trans. Med. Imaging 42, 1484\u20131494 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"1648_CR17","volume-title":"Subjective Logic: A Formalism for Reasoning Under Uncertainty","author":"A Jsang","year":"2018","unstructured":"Jsang, A.: Subjective Logic: A Formalism for Reasoning Under Uncertainty. Springer, Cham (2018)"},{"key":"1648_CR18","doi-asserted-by":"crossref","unstructured":"Jungo, A., Reyes, M.: Assessing reliability and challenges of uncertainty estimations for medical image segmentation. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13\u201317, 2019, Proceedings, Part II 22, pp. 48\u201356. Springer (2019)","DOI":"10.1007\/978-3-030-32245-8_6"},{"key":"1648_CR19","unstructured":"Kendall, A., Badrinarayanan, V., Cipolla, R.: Bayesian SegNet: model uncertainty in deep convolutional encoder-decoder architectures for scene understanding. arXiv preprint arXiv:1511.02680 (2015)"},{"key":"1648_CR20","doi-asserted-by":"crossref","unstructured":"Kerfoot, E., Clough, J., Oksuz, I., Lee, J., King, A.P., Schnabel, J.A.: Left-ventricle quantification using residual u-net. In: Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges: 9th International Workshop, STACOM 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers 9, pp. 371\u2013380. Springer (2019)","DOI":"10.1007\/978-3-030-12029-0_40"},{"key":"1648_CR21","unstructured":"Kohl, S., Romera-Paredes, B., Meyer, C., De\u00a0Fauw, J., Ledsam, J.R., Maier-Hein, K., Eslami, S., Jimenez\u00a0Rezende, D., Ronneberger, O.: A probabilistic u-net for segmentation of ambiguous images. In: Advances in Neural Information Processing Systems, Vol. 31 (2018)"},{"key":"1648_CR22","volume-title":"Continuous Multivariate Distributions, Volume 1: Models and Applications","author":"S Kotz","year":"2019","unstructured":"Kotz, S., Balakrishnan, N., Johnson, N.L.: Continuous Multivariate Distributions, Volume 1: Models and Applications, vol. 334. Wiley, Hoboken (2019)"},{"key":"1648_CR23","unstructured":"Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"1648_CR24","first-page":"8577","volume":"33","author":"B Li","year":"2019","unstructured":"Li, B., Liu, Y., Wang, X.: Gradient harmonized single-stage detector. Proc. AAAI Conf. Artif. Intell. 33, 8577\u20138584 (2019)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"issue":"30","key":"1648_CR25","doi-asserted-by":"publisher","first-page":"22071","DOI":"10.1007\/s00521-022-08016-4","volume":"35","author":"H Li","year":"2023","unstructured":"Li, H., Nan, Y., Del Ser, J., Yang, G.: Region-based evidential deep learning to quantify uncertainty and improve robustness of brain tumor segmentation. Neural Comput. Appl. 35(30), 22071\u201322085 (2023)","journal-title":"Neural Comput. Appl."},{"key":"1648_CR26","first-page":"1504","volume":"37","author":"Z Li","year":"2023","unstructured":"Li, Z., Li, X., Yang, L., Zhao, B., Song, R., Luo, L., Li, J., Yang, J.: Curriculum temperature for knowledge distillation. Proc. AAAI Conf. Artif. Intell. 37, 1504\u20131512 (2023)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"1648_CR27","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"issue":"3","key":"1648_CR28","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1162\/neco.1992.4.3.448","volume":"4","author":"DJ MacKay","year":"1992","unstructured":"MacKay, D.J.: A practical Bayesian framework for backpropagation networks. Neural Comput. 4(3), 448\u2013472 (1992)","journal-title":"Neural Comput."},{"issue":"6","key":"1648_CR29","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.7.6.064006","volume":"7","author":"S M\u00fcller","year":"2020","unstructured":"M\u00fcller, S., Weickert, J., Graf, N.: Robustness of brain tumor segmentation. J. Med. Imaging 7(6), 064006 (2020)","journal-title":"J. Med. Imaging"},{"key":"1648_CR30","doi-asserted-by":"crossref","unstructured":"Myronenko, A.: 3d mri brain tumor segmentation using autoencoder regularization. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part II 4, pp. 311\u2013320. Springer (2019)","DOI":"10.1007\/978-3-030-11726-9_28"},{"key":"1648_CR31","unstructured":"Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et\u00a0al.: Attention U-Net: learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)"},{"key":"1648_CR32","unstructured":"Sensoy, M., Kaplan, L., Kandemir, M.: Evidential deep learning to quantify classification uncertainty. In: Advances in Neural Information Processing Systems, vol. 31 (2018)"},{"key":"1648_CR33","doi-asserted-by":"crossref","unstructured":"Shrivastava, A., Gupta, A., Girshick, R.: Training region-based object detectors with online hard example mining. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 761\u2013769 (2016)","DOI":"10.1109\/CVPR.2016.89"},{"key":"1648_CR34","unstructured":"Smith, L., Gal, Y.: Understanding measures of uncertainty for adversarial example detection. arXiv preprint arXiv:1803.08533 (2018)"},{"issue":"1","key":"1648_CR35","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"1648_CR36","unstructured":"Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"1648_CR37","unstructured":"Van\u00a0Amersfoort, J., Smith, L., Teh, Y.W., Gal, Y.: Uncertainty estimation using a single deep deterministic neural network. In: International Conference on Machine Learning, pp. 9690\u20139700. PMLR (2020)"},{"key":"1648_CR38","doi-asserted-by":"crossref","unstructured":"Zou, K., Yuan, X., Shen, X., Wang, M., Fu, H.: TBraTS: trusted brain tumor segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 503\u2013513. Springer (2022)","DOI":"10.1007\/978-3-031-16452-1_48"}],"container-title":["Journal of Real-Time Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-025-01648-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11554-025-01648-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-025-01648-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,3]],"date-time":"2025-05-03T06:23:03Z","timestamp":1746253383000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11554-025-01648-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,18]]},"references-count":38,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,4]]}},"alternative-id":["1648"],"URL":"https:\/\/doi.org\/10.1007\/s11554-025-01648-4","relation":{},"ISSN":["1861-8200","1861-8219"],"issn-type":[{"type":"print","value":"1861-8200"},{"type":"electronic","value":"1861-8219"}],"subject":[],"published":{"date-parts":[[2025,3,18]]},"assertion":[{"value":"20 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 February 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 March 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":"The authors have no conflict of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"85"}}