{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T17:49:20Z","timestamp":1776275360590,"version":"3.50.1"},"publisher-location":"Cham","reference-count":51,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031438974","type":"print"},{"value":"9783031438981","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-43898-1_46","type":"book-chapter","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T23:08:23Z","timestamp":1696115303000},"page":"475-485","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Dice Semimetric Losses: Optimizing the\u00a0Dice Score with\u00a0Soft Labels"],"prefix":"10.1007","author":[{"given":"Zifu","family":"Wang","sequence":"first","affiliation":[]},{"given":"Teodora","family":"Popordanoska","sequence":"additional","affiliation":[]},{"given":"Jeroen","family":"Bertels","sequence":"additional","affiliation":[]},{"given":"Robin","family":"Lemmens","sequence":"additional","affiliation":[]},{"given":"Matthew B.","family":"Blaschko","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"key":"46_CR1","doi-asserted-by":"crossref","unstructured":"Abraham, N., Khan, N.M.: A novel focal Tversky loss function with improved attention U-Net for lesion segmentation. In: ISBI (2019)","DOI":"10.1109\/ISBI.2019.8759329"},{"key":"46_CR2","doi-asserted-by":"crossref","unstructured":"Berman, M., Triki, A.R., Blaschko, M.B.: The Lovasz-softmax loss: a tractable surrogate for the optimization of the intersection-over-union measure in neural networks. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00464"},{"key":"46_CR3","first-page":"101833","volume":"67","author":"J Bertels","year":"2021","unstructured":"Bertels, J., Robben, D., Vandermeulen, D., Suetens, P.: Theoretical analysis and experimental validation of volume bias of soft dice optimized segmentation maps in the context of inherent uncertainty. MIA 67, 101833 (2021)","journal-title":"MIA"},{"key":"46_CR4","first-page":"102680","volume":"84","author":"P Bilic","year":"2023","unstructured":"Bilic, P., et al.: The liver tumor segmentation benchmark (LiTS). MIA 84, 102680 (2023)","journal-title":"MIA"},{"key":"46_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1007\/978-3-030-01234-2_49","volume-title":"Computer Vision \u2013 ECCV 2018","author":"L-C Chen","year":"2018","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 833\u2013851. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_49"},{"key":"46_CR6","unstructured":"Contributors, M.: MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark (2020). https:\/\/github.com\/open-mmlab\/mmsegmentation"},{"key":"46_CR7","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: CVPR (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"46_CR8","doi-asserted-by":"publisher","unstructured":"Deza, M.M., Deza, E.: Encyclopedia of Distances. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-00234-2","DOI":"10.1007\/978-3-642-00234-2"},{"key":"46_CR9","first-page":"3679","volume":"39","author":"T Eelbode","year":"2020","unstructured":"Eelbode, T., et al.: Optimization for medical image segmentation: theory and practice when evaluating with dice score or Jaccard index. TMI 39, 3679\u20133690 (2020)","journal-title":"TMI"},{"key":"46_CR10","unstructured":"Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In: ICML (2016)"},{"key":"46_CR11","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.tcs.2017.01.004","volume":"718","author":"A Gragera","year":"2018","unstructured":"Gragera, A., Suppakitpaisarn, V.: Relaxed triangle inequality ratio of the S\u00f8rensen-Dice and Tversky indexes. TCS 718, 37\u201345 (2018)","journal-title":"TCS"},{"key":"46_CR12","first-page":"102038","volume":"71","author":"C Gros","year":"2021","unstructured":"Gros, C., Lemay, A., Cohen-Adad, J.: SoftSeg: advantages of soft versus binary training for image segmentation. MIA 71, 102038 (2021)","journal-title":"MIA"},{"key":"46_CR13","doi-asserted-by":"crossref","unstructured":"Guan, M.Y., Gulshan, V., Dai, A.M., Hinton, G.E.: Who said what: modeling individual labelers improves classification. In: AAAI (2018)","DOI":"10.1609\/aaai.v32i1.11756"},{"key":"46_CR14","unstructured":"Guo, C., Pleiss, G., Sun, Y., Weinberger, K.Q.: On calibration of modern neural networks. In: ICML (2017)"},{"key":"46_CR15","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"46_CR16","first-page":"101821","volume":"67","author":"N Heller","year":"2021","unstructured":"Heller, N., et al.: The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: results of the KiTS19 challenge. MIA 67, 101821 (2021)","journal-title":"MIA"},{"key":"46_CR17","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. In: NeurIPS Workshop (2015)"},{"key":"46_CR18","unstructured":"Huang, T., et al.: Masked distillation with receptive tokens. In: ICLR (2023)"},{"key":"46_CR19","unstructured":"Iakubovskii, P.: Segmentation models PyTorch (2019). https:\/\/github.com\/qubvel\/segmentation_models.pytorch"},{"key":"46_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, P.F., Kohl, S.A.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Meth. 18, 203\u2013211 (2021)","journal-title":"Nat. Meth."},{"key":"46_CR21","doi-asserted-by":"crossref","unstructured":"Islam, M., Glocker, B.: Spatially varying label smoothing: capturing uncertainty from expert annotations. In: IPMI (2021)","DOI":"10.1007\/978-3-030-78191-0_52"},{"key":"46_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"540","DOI":"10.1007\/978-3-030-32251-9_59","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"MH Jensen","year":"2019","unstructured":"Jensen, M.H., J\u00f8rgensen, D.R., Jalaboi, R., Hansen, M.E., Olsen, M.A.: Improving uncertainty estimation in convolutional neural networks using inter-rater agreement. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11767, pp. 540\u2013548. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32251-9_59"},{"key":"46_CR23","doi-asserted-by":"crossref","unstructured":"Ji, W., et al.: Learning calibrated medical image segmentation via multi-rater agreement modeling. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.01216"},{"key":"46_CR24","doi-asserted-by":"crossref","unstructured":"Kirillov, A., et al.: Segment anything. In: ICCV (2023)","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"46_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.59275\/j.melba.2022-db5c","volume":"031","author":"A Lemay","year":"2023","unstructured":"Lemay, A., Gros, C., Karthik, E.N., Cohen-Adad, J.: Label fusion and training methods for reliable representation of inter-rater uncertainty. MELBA 031, 1\u201329 (2023)","journal-title":"MELBA"},{"key":"46_CR26","doi-asserted-by":"crossref","unstructured":"Li, X., Sun, X., Meng, Y., Liang, J., Wu, F., Li, J.: Dice loss for data-imbalanced NLP tasks. In: ACL (2020)","DOI":"10.18653\/v1\/2020.acl-main.45"},{"key":"46_CR27","unstructured":"Maier-Hein, L., et al.: Metrics reloaded: recommendations for image analysis validation. arXiv (2023)"},{"key":"46_CR28","unstructured":"Menon, A.K., Rawat, A.S., Reddi, S.J., Kim, S., Kumar, S.: A Statistical Perspective on Distillation. In: ICML (2021)"},{"key":"46_CR29","unstructured":"Menze, B., Joskowicz, L., Bakas, S., Jakab, A., Konukoglu, E., Becker, A.: Quantification of uncertainties in biomedical image quantification challenge. In: MICCAI (2020). https:\/\/qubiq.grand-challenge.org"},{"key":"46_CR30","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., Ahmadi, S.A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 3DV (2016)","DOI":"10.1109\/3DV.2016.79"},{"key":"46_CR31","unstructured":"M\u00fcller, R., Kornblith, S., Hinton, G.: When does label smoothing help? In: NeurIPS (2019)"},{"key":"46_CR32","unstructured":"Nordstr\u00f6m, M., Hult, H., Maki, A., L\u00f6fman, F.: Noisy image segmentation with soft-dice. arXiv (2023)"},{"key":"46_CR33","doi-asserted-by":"crossref","unstructured":"Nowozin, S.: Optimal decisions from probabilistic models: the intersection-over-union case. In: CVPR (2014)","DOI":"10.1109\/CVPR.2014.77"},{"key":"46_CR34","doi-asserted-by":"crossref","unstructured":"Popordanoska, T., Bertels, J., Vandermeulen, D., Maes, F., Blaschko, M.B.: On the relationship between calibrated predictors and unbiased volume estimation. In: MICCAI (2021)","DOI":"10.1007\/978-3-030-87193-2_64"},{"key":"46_CR35","unstructured":"Popordanoska, T., Sayer, R., Blaschko, M.B.: A consistent and differentiable Lp canonical calibration error estimator. In: NeurIPS (2022)"},{"key":"46_CR36","doi-asserted-by":"crossref","unstructured":"Qin, D., et al.: Efficient medical image segmentation based on knowledge distillation. TMI (2021)","DOI":"10.1109\/TMI.2021.3098703"},{"key":"46_CR37","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-50835-1_22","volume-title":"Advances in Visual Computing","author":"MA Rahman","year":"2016","unstructured":"Rahman, M.A., Wang, Y.: Optimizing intersection-over-union in deep neural networks for image segmentation. In: Bebis, G., et al. (eds.) ISVC 2016. LNCS, vol. 10072, pp. 234\u2013244. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-50835-1_22"},{"key":"46_CR38","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: MICCAI (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"46_CR39","doi-asserted-by":"crossref","unstructured":"Salehi, S.S.M., Erdogmus, D., Gholipour, A.: Tversky loss function for image segmentation using 3D fully convolutional deep networks. In: MICCAI Workshop (2017)","DOI":"10.1007\/978-3-319-67389-9_44"},{"key":"46_CR40","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobileNetV2: inverted residuals and linear bottlenecks. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"46_CR41","unstructured":"Silva, J.L., Oliveira, A.L.: Using soft labels to model uncertainty in medical image segmentation. In: MICCAI Workshop (2021)"},{"key":"46_CR42","doi-asserted-by":"crossref","unstructured":"Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., Cardoso, M.J.: Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations. In: MICCAI Workshop (2017)","DOI":"10.1007\/978-3-319-67558-9_28"},{"key":"46_CR43","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"46_CR44","unstructured":"Tan, M., Le, Q.V.: EfficientNet: rethinking model scaling for convolutional neural networks. In: ICML (2019)"},{"key":"46_CR45","doi-asserted-by":"crossref","unstructured":"Tilborghs, S., Bertels, J., Robben, D., Vandermeulen, D., Maes, F.: The dice loss in the context of missing or empty labels: introducing $$\\Phi $$ and $$\\epsilon $$. In: MICCAI (2022)","DOI":"10.1007\/978-3-031-16443-9_51"},{"key":"46_CR46","doi-asserted-by":"publisher","unstructured":"Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995). https:\/\/doi.org\/10.1007\/978-1-4757-3264-1","DOI":"10.1007\/978-1-4757-3264-1"},{"key":"46_CR47","unstructured":"Wang, Z., Blaschko, M.B.: Jaccard metric losses: optimizing the Jaccard index with soft labels. arXiv (2023)"},{"key":"46_CR48","unstructured":"Wightman, R.: Pytorch image models (2019). https:\/\/github.com\/rwightman\/pytorch-image-models"},{"key":"46_CR49","doi-asserted-by":"publisher","first-page":"735","DOI":"10.1109\/TPAMI.2018.2883039","volume":"42","author":"J Yu","year":"2018","unstructured":"Yu, J., Blaschko, M.B.: The Lov\u00e1sz hinge: a novel convex surrogate for submodular losses. TPAMI 42, 735\u2013748 (2018)","journal-title":"TPAMI"},{"key":"46_CR50","unstructured":"Yu, J., et al.: Learning generalized intersection over union for dense pixelwise prediction. In: ICML (2021)"},{"key":"46_CR51","unstructured":"Zhang, D., et al.: Deep learning for medical image segmentation: tricks, challenges and future directions. arXiv (2022)"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43898-1_46","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T14:25:30Z","timestamp":1710167130000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43898-1_46"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031438974","9783031438981"],"references-count":51,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43898-1_46","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2023\/en\/","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":"2250","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":"730","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":"32% - 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":"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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}