{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T16:19:06Z","timestamp":1747153146555,"version":"3.40.5"},"publisher-location":"Cham","reference-count":46,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031090011"},{"type":"electronic","value":"9783031090028"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-09002-8_52","type":"book-chapter","created":{"date-parts":[[2022,7,14]],"date-time":"2022-07-14T12:05:34Z","timestamp":1657800334000},"page":"585-596","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Using Soft Labels to\u00a0Model Uncertainty in\u00a0Medical Image Segmentation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6475-3042","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Louren\u00e7o-Silva","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8638-5594","authenticated-orcid":false,"given":"Arlindo L.","family":"Oliveira","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,7,15]]},"reference":[{"key":"52_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1007\/978-3-030-32245-8_14","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"CF Baumgartner","year":"2019","unstructured":"Baumgartner, C.F., et al.: PHiSeg: capturing uncertainty in medical image segmentation. In: Shen, D., Liu, T., Peters, T.M., Staib, L.H., Essert, C., Zhou, S., Yap, P.-T., Khan, A. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 119\u2013127. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_14"},{"issue":"1","key":"52_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1175\/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2","volume":"78","author":"GW Brier","year":"1950","unstructured":"Brier, G.W., et al.: Verification of forecasts expressed in terms of probability. Mon. Weather Rev. 78(1), 1\u20133 (1950)","journal-title":"Mon. Weather Rev."},{"key":"52_CR3","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 248\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"52_CR4","unstructured":"Fort, S., Brock, A., Pascanu, R., De, S., Smith, S.L.: Drawing multiple augmentation samples per image during training efficiently decreases test error. arXiv preprint 2105.13343 (2021)"},{"key":"52_CR5","doi-asserted-by":"crossref","unstructured":"Friedman, J., Hastie, T., Tibshirani, R., et al.: The elements of statistical learning, vol. 1. Springer Series in Statistics New York (2001)","DOI":"10.1007\/978-0-387-21606-5_1"},{"key":"52_CR6","unstructured":"Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249\u2013256. JMLR Workshop and Conference Proceedings (2010)"},{"key":"52_CR7","unstructured":"Guzman-Rivera, A., Batra, D., Kohli, P.: Multiple choice learning: learning to produce multiple structured outputs. In: Advances in Neural Information Processing Systems, vol. 1, p. 3 (2012)"},{"key":"52_CR8","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1026\u20131034. IEEE (2015)","DOI":"10.1109\/ICCV.2015.123"},{"key":"52_CR9","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"52_CR10","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint 1503.02531 (2015)"},{"key":"52_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/978-3-030-32245-8_16","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"S Hu","year":"2019","unstructured":"Hu, S., Worrall, D., Knegt, S., Veeling, B., Huisman, H., Welling, M.: Supervised uncertainty quantification for segmentation with multiple annotations. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 137\u2013145. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_16"},{"key":"52_CR12","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"52_CR13","doi-asserted-by":"crossref","unstructured":"Ilg, E., \u00c7i\u00e7ek, \u00d6., Galesso, S., Klein, A., Makansi, O., Hutter, F., Brox, T.: Uncertainty estimates for optical flow with multi-hypotheses networks. arXiv preprint 1802.07095 p. 81 (2018)","DOI":"10.1007\/978-3-030-01234-2_40"},{"key":"52_CR14","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448\u2013456 (2015)"},{"issue":"3","key":"52_CR15","doi-asserted-by":"publisher","first-page":"1391","DOI":"10.1007\/s00330-018-5695-5","volume":"29","author":"L Joskowicz","year":"2019","unstructured":"Joskowicz, L., Cohen, D., Caplan, N., Sosna, J.: Inter-observer variability of manual contour delineation of structures in ct. Eur. Radiol. 29(3), 1391\u20131399 (2019)","journal-title":"Eur. Radiol."},{"key":"52_CR16","unstructured":"Kendall, A., Badrinarayanan, V., Cipolla, R.: Bayesian SegNet: Model uncertainty in deep convolutional encoder-decoder architectures for scene understanding. arXiv preprint 1511.02680 (2015)"},{"key":"52_CR17","unstructured":"Kendall, A., Gal, Y.: What uncertainties do we need in Bayesian deep learning for computer vision? arXiv preprint 1703.04977 (2017)"},{"key":"52_CR18","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint 1412.6980 (2014)"},{"key":"52_CR19","unstructured":"Kingma, D.P., Mohamed, S., Rezende, D.J., Welling, M.: Semi-supervised learning with deep generative models. In: Advances in Neural Information Processing Systems, pp. 3581\u20133589 (2014)"},{"key":"52_CR20","first-page":"6114","volume":"1312","author":"DP Kingma","year":"2013","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational Bayes. arXiv preprint 1312, 6114 (2013)","journal-title":"Auto-encoding variational Bayes. arXiv preprint"},{"key":"52_CR21","first-page":"2575","volume":"28","author":"DP Kingma","year":"2015","unstructured":"Kingma, D.P., Salimans, T., Welling, M.: Variational dropout and the local reparameterization trick. Adv. Neural. Inf. Process. Syst. 28, 2575\u20132583 (2015)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"52_CR22","unstructured":"Kohl, S.A., et al.: A hierarchical probabilistic U-Net for modeling multi-scale ambiguities. arXiv preprint 1905.13077 (2019)"},{"key":"52_CR23","unstructured":"Kohl, S.A., et al.: A probabilistic U-Net for segmentation of ambiguous images. arXiv preprint 1806.05034 (2018)"},{"key":"52_CR24","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/j.patrec.2018.12.007","volume":"120","author":"S Kosub","year":"2019","unstructured":"Kosub, S.: A note on the triangle inequality for the Jaccard distance. Pattern Recogn. Lett. 120, 36\u201338 (2019)","journal-title":"Pattern Recogn. Lett."},{"key":"52_CR25","first-page":"1097","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Adv. Neural. Inf. Process. Syst. 25, 1097\u20131105 (2012)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"52_CR26","unstructured":"Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. arXiv preprint 1612.01474 (2016)"},{"issue":"11","key":"52_CR27","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"key":"52_CR28","unstructured":"Lee, S., Prakash, S.P.S., Cogswell, M., Ranjan, V., Crandall, D., Batra, D.: Stochastic multiple choice learning for training diverse deep ensembles. In: Advances in Neural Information Processing Systems, pp. 2119\u20132127 (2016)"},{"key":"52_CR29","unstructured":"Lee, S., Purushwalkam, S., Cogswell, M., Crandall, D., Batra, D.: Why M heads are better than one: training a diverse ensemble of deep networks. arXiv preprint 1511.06314 (2015)"},{"key":"52_CR30","unstructured":"Lei, T., Wang, R., Wan, Y., Zhang, B., Meng, H., Nandi, A.K.: Medical image segmentation using deep learning: a survey. arXiv preprint 2009.13120 (2020)"},{"issue":"1","key":"52_CR31","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1023\/A:1019154432472","volume":"26","author":"AH Lipkus","year":"1999","unstructured":"Lipkus, A.H.: A proof of the triangle inequality for the Tanimoto distance. J. Math. Chem. 26(1), 263\u2013265 (1999)","journal-title":"J. Math. Chem."},{"key":"52_CR32","unstructured":"Loshchilov, I., Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint 1608.03983 (2016)"},{"key":"52_CR33","unstructured":"Monteiro, M., Folgoc, L.L., de Castro, D.C., Pawlowski, N., Marques, B., Kamnitsas, K., van der Wilk, M., Glocker, B.: Stochastic segmentation networks: modelling spatially correlated aleatoric uncertainty. arXiv preprint 2006.06015 (2020)"},{"key":"52_CR34","unstructured":"Naeini, M.P., Cooper, G., Hauskrecht, M.: Obtaining well calibrated probabilities using Bayesian binning. In: Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)"},{"key":"52_CR35","doi-asserted-by":"crossref","unstructured":"Pham, H., Dai, Z., Xie, Q., Luong, M.T., Le, Q.V.: Meta pseudo labels (2021)","DOI":"10.1109\/CVPR46437.2021.01139"},{"key":"52_CR36","unstructured":"Rezende, D.J., Mohamed, S., Wierstra, D.: Stochastic backpropagation and approximate inference in deep generative models. In: International Conference on Machine Learning, pp. 1278\u20131286. PMLR (2014)"},{"key":"52_CR37","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"52_CR38","doi-asserted-by":"crossref","unstructured":"Rupprecht, C., Laina, I., DiPietro, R., Baust, M., Tombari, F., Navab, N., Hager, G.D.: Learning in an uncertain world: representing ambiguity through multiple hypotheses. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3591\u20133600 (2017)","DOI":"10.1109\/ICCV.2017.388"},{"key":"52_CR39","unstructured":"Silva, J.L., Menezes, M.N., Rodrigues, T., Silva, B., Pinto, F.J., Oliveira, A.L.: Encoder-decoder architectures for clinically relevant coronary artery segmentation. arXiv preprint 2106.11447 (2021)"},{"key":"52_CR40","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint 1409.1556 (2014)"},{"key":"52_CR41","first-page":"3483","volume":"28","author":"K Sohn","year":"2015","unstructured":"Sohn, K., Lee, H., Yan, X.: Learning structured output representation using deep conditional generative models. Adv. Neural. Inf. Process. Syst. 28, 3483\u20133491 (2015)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"1","key":"52_CR42","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":"52_CR43","doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u20139 (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"52_CR44","unstructured":"Tan, M., Le, Q.: EfficientNet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105\u20136114 (2019)"},{"key":"52_CR45","doi-asserted-by":"crossref","unstructured":"Xie, Q., Luong, M.T., Hovy, E., Le, Q.V.: Self-training with noisy student improves ImageNet classification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10687\u201310698 (2020)","DOI":"10.1109\/CVPR42600.2020.01070"},{"key":"52_CR46","unstructured":"Yakubovskiy, P.: Segmentation models pytorch (2020). https:\/\/github.com\/qubvel\/segmentation_models.pytorch"}],"container-title":["Lecture Notes in Computer Science","Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-09002-8_52","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,14]],"date-time":"2022-07-14T12:14:34Z","timestamp":1657800874000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-09002-8_52"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031090011","9783031090028"],"references-count":46,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-09002-8_52","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"15 July 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BrainLes","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International MICCAI Brainlesion Workshop","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwb2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.brainlesion-workshop.org\/?msclkid=7759e32ed14111ecba82c5ba435279db","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"151","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"91","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"60% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1.5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}