{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T16:26:36Z","timestamp":1778257596808,"version":"3.51.4"},"publisher-location":"Cham","reference-count":49,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031730009","type":"print"},{"value":"9783031730016","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T00:00:00Z","timestamp":1732665600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T00:00:00Z","timestamp":1732665600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-73001-6_27","type":"book-chapter","created":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T10:21:59Z","timestamp":1732616519000},"page":"473-490","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Gradient-Aware for\u00a0Class-Imbalanced Semi-supervised Medical Image Segmentation"],"prefix":"10.1007","author":[{"given":"Wenbo","family":"Qi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiafei","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S. C.","family":"Chan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,27]]},"reference":[{"key":"27_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: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 683\u2013687. IEEE (2019)","DOI":"10.1109\/ISBI.2019.8759329"},{"key":"27_CR2","doi-asserted-by":"crossref","unstructured":"Bai, Y., Chen, D., Li, Q., Shen, W., Wang, Y.: Bidirectional copy-paste for semi-supervised medical image segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11514\u201311524 (2023)","DOI":"10.1109\/CVPR52729.2023.01108"},{"key":"27_CR3","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1007\/978-3-031-16452-1_22","volume-title":"Medical Image Computing and Computer-Assisted Intervention","author":"H Basak","year":"2022","unstructured":"Basak, H., Ghosal, S., Sarkar, R.: Addressing class imbalance in semi-supervised image segmentation: a study on cardiac MRI. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13438, pp. 224\u2013233. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16452-1_22"},{"issue":"11","key":"27_CR4","doi-asserted-by":"publisher","first-page":"2514","DOI":"10.1109\/TMI.2018.2837502","volume":"37","author":"O Bernard","year":"2018","unstructured":"Bernard, O., et al.: Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Trans. Med. Imaging 37(11), 2514\u20132525 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"27_CR5","doi-asserted-by":"crossref","unstructured":"Cai, H., Li, S., Qi, L., Yu, Q., Shi, Y., Gao, Y.: Orthogonal annotation benefits barely-supervised medical image segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3302\u20133311 (2023)","DOI":"10.1109\/CVPR52729.2023.00322"},{"key":"27_CR6","unstructured":"Chen, B., Jiang, J., Wang, X., Wan, P., Wang, J., Long, M.: Debiased self-training for semi-supervised learning. In: Advances in Neural Information Processing Systems 35, pp. 32424\u201332437 (2022)"},{"key":"27_CR7","doi-asserted-by":"crossref","unstructured":"Chen, D., Bai, Y., Shen, W., Li, Q., Yu, L., Wang, Y.: MagicNet: semi-supervised multi-organ segmentation via magic-cube partition and recovery. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 23869\u201323878 (2023)","DOI":"10.1109\/CVPR52729.2023.02286"},{"key":"27_CR8","unstructured":"Chen, H., et al.: An embarrassingly simple baseline for imbalanced semi-supervised learning. arXiv preprint arXiv:2211.11086 (2022)"},{"key":"27_CR9","doi-asserted-by":"crossref","unstructured":"Chen, X., Yuan, Y., Zeng, G., Wang, J.: Semi-supervised semantic segmentation with cross pseudo supervision. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2613\u20132622 (2021)","DOI":"10.1109\/CVPR46437.2021.00264"},{"key":"27_CR10","doi-asserted-by":"crossref","unstructured":"Chen, D.-D., Wang, W., Gao, W., Zhou, Z.H.: Tri-net for semi-supervised deep learning. In: Proceedings of Twenty-Seventh International Joint Conference on Artificial Intelligence, pp. 2014\u20132020 (2018)","DOI":"10.24963\/ijcai.2018\/278"},{"key":"27_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1007\/978-3-319-46976-8_19","volume-title":"Deep Learning and Data Labeling for Medical Applications","author":"M Drozdzal","year":"2016","unstructured":"Drozdzal, M., Vorontsov, E., Chartrand, G., Kadoury, S., Pal, C.: The importance of skip connections in biomedical image segmentation. In: Carneiro, G., et al. (eds.) LABELS\/DLMIA -2016. LNCS, vol. 10008, pp. 179\u2013187. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46976-8_19"},{"key":"27_CR12","doi-asserted-by":"publisher","first-page":"714","DOI":"10.1007\/978-3-031-43898-1_68","volume-title":"Medical Image Computing and Computer-Assisted Intervention","author":"A Gonzalez-Jimenez","year":"2023","unstructured":"Gonzalez-Jimenez, A., Lionetti, S., Gottfrois, P., Gr\u00f6ger, F., Pouly, M., Navarini, A.A.: Robust T-loss for medical image segmentation. In: Greenspan, H., et al. (eds.) MICCAI 2023. LNCS, vol. 14222, pp. 714\u2013724. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-43898-1_68"},{"key":"27_CR13","unstructured":"Guo, L.Z., Li, Y.F.: Class-imbalanced semi-supervised learning with adaptive thresholding. In: International Conference on Machine Learning, pp. 8082\u20138094. PMLR (2022)"},{"key":"27_CR14","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: Advances in Neural Information Processing Systems 33, pp. 6840\u20136851 (2020)"},{"key":"27_CR15","doi-asserted-by":"crossref","unstructured":"Hong, Y., Han, S., Choi, K., Seo, S., Kim, B., Chang, B.: Disentangling label distribution for long-tailed visual recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6626\u20136636 (2021)","DOI":"10.1109\/CVPR46437.2021.00656"},{"key":"27_CR16","unstructured":"Ji, Y., et al.: AMOS: a large-scale abdominal multi-organ benchmark for versatile medical image segmentation. In: Advances in Neural Information Processing Systems 35, pp. 36722\u201336732 (2022)"},{"issue":"2","key":"27_CR17","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1109\/TMI.2019.2930068","volume":"39","author":"D Karimi","year":"2019","unstructured":"Karimi, D., Salcudean, S.E.: Reducing the hausdorff distance in medical image segmentation with convolutional neural networks. IEEE Trans. Med. Imaging 39(2), 499\u2013513 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"27_CR18","unstructured":"Kervadec, H., Bouchtiba, J., Desrosiers, C., Granger, E., Dolz, J., Ayed, I.B.: Boundary loss for highly unbalanced segmentation. In: International Conference on Medical Imaging with Deep Learning, pp. 285\u2013296. PMLR (2019)"},{"key":"27_CR19","unstructured":"Kervadec, H., de\u00a0Bruijne, M.: On the dice loss gradient and the ways to mimic it. arXiv preprint arXiv:2304.04319 (2023)"},{"key":"27_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1007\/978-3-030-12939-2_8","volume-title":"Pattern Recognition","author":"O Kodym","year":"2019","unstructured":"Kodym, O., \u0160pan\u011bl, M., Herout, A.: Segmentation of head and neck organs at risk using CNN with batch dice loss. In: Brox, T., Bruhn, A., Fritz, M. (eds.) GCPR 2018. LNCS, vol. 11269, pp. 105\u2013114. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-12939-2_8"},{"key":"27_CR21","doi-asserted-by":"crossref","unstructured":"Lai, Z., Wang, C., Cheung, S.C., Chuah, C.N.: SAR: self-adaptive refinement on pseudo labels for multiclass-imbalanced semi-supervised learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4091\u20134100 (2022)","DOI":"10.1109\/CVPRW56347.2022.00454"},{"key":"27_CR22","unstructured":"Landman, B., Xu, Z., Igelsias, J.E., Styner, M., Langerak, T.R., Klein, A.: 2015 MICCAI multi-atlas labeling beyond the cranial vault workshop and challenge. In: Proceedings of the MICCAI Multi-Atlas Labeling Beyond Cranial Vault\u2014Workshop Challenge (2015)"},{"key":"27_CR23","unstructured":"Lee, D.H., et\u00a0al.: Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks. In: Workshop on Challenges in Representation Learning, ICML, Atlanta, vol.\u00a03, p.\u00a0896 (2013)"},{"key":"27_CR24","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"},{"key":"27_CR25","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1007\/978-3-031-16452-1_11","volume-title":"Medical Image Computing and Computer-Assisted Intervention","author":"Y Lin","year":"2022","unstructured":"Lin, Y., Yao, H., Li, Z., Zheng, G., Li, X.: Calibrating label distribution for class-imbalanced barely-supervised knee segmentation. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13438, pp. 109\u2013118. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16452-1_11"},{"key":"27_CR26","unstructured":"Luo, X.: SSL4MIS (2020). https:\/\/github.com\/HiLab-git\/SSL4MIS"},{"key":"27_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102035","volume":"71","author":"J Ma","year":"2021","unstructured":"Ma, J., et al.: Loss odyssey in medical image segmentation. Med. Image Anal. 71, 102035 (2021)","journal-title":"Med. Image Anal."},{"key":"27_CR28","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565\u2013571. IEEE (2016)","DOI":"10.1109\/3DV.2016.79"},{"key":"27_CR29","doi-asserted-by":"crossref","unstructured":"Oh, Y., Kim, D.J., Kweon, I.S.: DASO: distribution-aware semantics-oriented pseudo-label for imbalanced semi-supervised learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9786\u20139796 (2022)","DOI":"10.1109\/CVPR52688.2022.00956"},{"issue":"7","key":"27_CR30","doi-asserted-by":"publisher","first-page":"724","DOI":"10.1038\/s42256-023-00682-w","volume":"5","author":"H Peiris","year":"2023","unstructured":"Peiris, H., Hayat, M., Chen, Z., Egan, G., Harandi, M.: Uncertainty-guided dual-views for semi-supervised volumetric medical image segmentation. Nat. Mach. Intell. 5(7), 724\u2013738 (2023)","journal-title":"Nat. Mach. Intell."},{"key":"27_CR31","doi-asserted-by":"publisher","first-page":"4842","DOI":"10.1109\/TIP.2023.3304518","volume":"32","author":"W Qi","year":"2023","unstructured":"Qi, W., Wu, H., Chan, S.: MDF-Net: a multi-scale dynamic fusion network for breast tumor segmentation of ultrasound images. IEEE Trans. Image Process. 32, 4842\u20134855 (2023)","journal-title":"IEEE Trans. Image Process."},{"key":"27_CR32","unstructured":"Rizve, M.N., Duarte, K., Rawat, Y.S., Shah, M.: In defense of pseudo-labeling: an uncertainty-aware pseudo-label selection framework for semi-supervised learning. arXiv preprint arXiv:2101.06329 (2021)"},{"key":"27_CR33","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, Part III. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"27_CR34","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1007\/978-3-319-67389-9_44","volume-title":"Machine Learning in Medical Imaging","author":"SSM Salehi","year":"2017","unstructured":"Salehi, S.S.M., Erdogmus, D., Gholipour, A.: Tversky loss function for image segmentation using 3D fully convolutional deep networks. In: Wang, Q., Shi, Y., Suk, H.-I., Suzuki, K. (eds.) MLMI 2017. LNCS, vol. 10541, pp. 379\u2013387. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-67389-9_44"},{"key":"27_CR35","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1007\/978-3-319-67558-9_28","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"CH Sudre","year":"2017","unstructured":"Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., Jorge Cardoso, M.: Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In: Cardoso, M.J., et al. (eds.) DLMIA\/ML-CDS-2017. LNCS, vol. 10553, pp. 240\u2013248. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-67558-9_28"},{"key":"27_CR36","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.compmedimag.2019.04.005","volume":"75","author":"SA Taghanaki","year":"2019","unstructured":"Taghanaki, S.A., et al.: Combo loss: handling input and output imbalance in multi-organ segmentation. Comput. Med. Imaging Graph. 75, 24\u201333 (2019)","journal-title":"Comput. Med. Imaging Graph."},{"issue":"10","key":"27_CR37","doi-asserted-by":"publisher","first-page":"480","DOI":"10.1038\/s42256-019-0099-z","volume":"1","author":"H Tang","year":"2019","unstructured":"Tang, H., et al.: Clinically applicable deep learning framework for organs at risk delineation in CT images. Nat. Mach. Intell. 1(10), 480\u2013491 (2019)","journal-title":"Nat. Mach. Intell."},{"key":"27_CR38","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 30 (2017)"},{"key":"27_CR39","doi-asserted-by":"publisher","first-page":"582","DOI":"10.1007\/978-3-031-43898-1_56","volume-title":"Medical Image Computing and Computer-Assisted Intervention","author":"H Wang","year":"2023","unstructured":"Wang, H., Li, X.: DHC: dual-debiased heterogeneous co-training framework for class-imbalanced semi-supervised medical image segmentation. In: Greenspan, H., et al. (eds.) MICCAI 2023. LNCS, vol. 14222, pp. 582\u2013591. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-43898-1_56"},{"key":"27_CR40","unstructured":"Wang, H., Li, X.: Towards generic semi-supervised framework for volumetric medical image segmentation. In: Advances in Neural Information Processing Systems 36 (2024)"},{"key":"27_CR41","doi-asserted-by":"crossref","unstructured":"Wang, X., Wu, Z., Lian, L., Yu, S.X.: Debiased learning from naturally imbalanced pseudo-labels. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14647\u201314657 (2022)","DOI":"10.1109\/CVPR52688.2022.01424"},{"key":"27_CR42","doi-asserted-by":"crossref","unstructured":"Wei, C., Sohn, K., Mellina, C., Yuille, A., Yang, F.: CReST: a class-rebalancing self-training framework for imbalanced semi-supervised learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10857\u201310866 (2021)","DOI":"10.1109\/CVPR46437.2021.01071"},{"key":"27_CR43","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"612","DOI":"10.1007\/978-3-030-00931-1_70","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"KCL Wong","year":"2018","unstructured":"Wong, K.C.L., Moradi, M., Tang, H., Syeda-Mahmood, T.: 3D segmentation with exponential logarithmic loss for highly unbalanced object sizes. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11072, pp. 612\u2013619. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00931-1_70"},{"key":"27_CR44","unstructured":"Wu, L., et al.: R-Drop: regularized dropout for neural networks. In: Advances in Neural Information Processing Systems 34, pp. 10890\u201310905 (2021)"},{"key":"27_CR45","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1007\/978-3-031-16443-9_4","volume-title":"Medical Image Computing and Computer-Assisted Intervention","author":"Y Wu","year":"2022","unstructured":"Wu, Y., Wu, Z., Wu, Q., Ge, Z., Cai, J.: Exploring smoothness and class-separation for semi-supervised medical image segmentation. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13435, pp. 34\u201343. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16443-9_4"},{"key":"27_CR46","unstructured":"Wu, Z., Shen, C., van den Hengel, A.: Bridging category-level and instance-level semantic image segmentation. arXiv preprint arXiv:1605.06885 (2016)"},{"issue":"9","key":"27_CR47","doi-asserted-by":"publisher","first-page":"2228","DOI":"10.1109\/TMI.2022.3161829","volume":"41","author":"C You","year":"2022","unstructured":"You, C., Zhou, Y., Zhao, R., Staib, L., Duncan, J.S.: SimCVD: simple contrastive voxel-wise representation distillation for semi-supervised medical image segmentation. IEEE Trans. Med. Imaging 41(9), 2228\u20132237 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"27_CR48","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"605","DOI":"10.1007\/978-3-030-32245-8_67","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"L Yu","year":"2019","unstructured":"Yu, L., Wang, S., Li, X., Fu, C.-W., Heng, P.-A.: Uncertainty-aware self-ensembling model for semi-supervised 3D left atrium segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 605\u2013613. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_67"},{"issue":"2","key":"27_CR49","doi-asserted-by":"publisher","first-page":"576","DOI":"10.1002\/mp.13300","volume":"46","author":"W Zhu","year":"2019","unstructured":"Zhu, W., et al.: AnatomyNet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Med. Phys. 46(2), 576\u2013589 (2019)","journal-title":"Med. Phys."}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73001-6_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T11:15:44Z","timestamp":1732619744000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73001-6_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,27]]},"ISBN":["9783031730009","9783031730016"],"references-count":49,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73001-6_27","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,27]]},"assertion":[{"value":"27 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}