{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T03:59:39Z","timestamp":1769831979163,"version":"3.49.0"},"publisher-location":"Cham","reference-count":65,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031732287","type":"print"},{"value":"9783031732294","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T00:00:00Z","timestamp":1729814400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T00:00:00Z","timestamp":1729814400000},"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\/978-3-031-73229-4_16","type":"book-chapter","created":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T15:03:09Z","timestamp":1729782189000},"page":"271-289","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Semi-supervised Segmentation of\u00a0Histopathology Images with\u00a0Noise-Aware Topological Consistency"],"prefix":"10.1007","author":[{"given":"Meilong","family":"Xu","sequence":"first","affiliation":[]},{"given":"Xiaoling","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Saumya","family":"Gupta","sequence":"additional","affiliation":[]},{"given":"Shahira","family":"Abousamra","sequence":"additional","affiliation":[]},{"given":"Chao","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,25]]},"reference":[{"key":"16_CR1","doi-asserted-by":"crossref","unstructured":"Basak, H., Yin, Z.: Pseudo-label guided contrastive learning for semi-supervised medical image segmentation. In: CVPR (2023)","DOI":"10.1109\/CVPR52729.2023.01895"},{"key":"16_CR2","unstructured":"Berthelot, D., Carlini, N., Goodfellow, I., Papernot, N., Oliver, A., Raffel, C.A.: Mixmatch: a holistic approach to semi-supervised learning. In: NeurIPS (2019)"},{"key":"16_CR3","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1007\/978-3-031-25066-8_9","volume-title":"ECCV 2022","author":"H Cao","year":"2022","unstructured":"Cao, H., et al.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds.) ECCV 2022. LNCS, vol. 13803, pp. 205\u2013218. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-25066-8_9"},{"key":"16_CR4","doi-asserted-by":"publisher","first-page":"118568","DOI":"10.1016\/j.neuroimage.2021.118568","volume":"224","author":"G Chen","year":"2021","unstructured":"Chen, G., et al.: MTANS: multi-scale mean teacher combined adversarial network with shape-aware embedding for semi-supervised brain lesion segmentation. NeuroImage 224, 118568 (2021)","journal-title":"NeuroImage"},{"key":"16_CR5","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: ECCV (2018)","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"16_CR6","doi-asserted-by":"crossref","unstructured":"Clough, J.R., Byrne, N., Oksuz, I., Zimmer, V.A., Schnabel, J.A., King, A.P.: A topological loss function for deep-learning based image segmentation using persistent homology. TPAMI (2020)","DOI":"10.1007\/978-3-030-20351-1_2"},{"key":"16_CR7","doi-asserted-by":"crossref","unstructured":"Cohen-Steiner, D., Edelsbrunner, H., Harer, J.: Stability of persistence diagrams. In: Proceedings of the Twenty-First Annual Symposium on Computational Geometry (2005)","DOI":"10.1145\/1064092.1064133"},{"key":"16_CR8","doi-asserted-by":"crossref","unstructured":"Cohen-Steiner, D., Edelsbrunner, H., Harer, J., Mileyko, Y.: Lipschitz functions have l p-stable persistence. Found. Comput. Math. (2010)","DOI":"10.1007\/s10208-010-9060-6"},{"key":"16_CR9","doi-asserted-by":"crossref","unstructured":"Edelsbrunner, Letscher, Zomorodian: Topological persistence and simplification. Discrete Comput. Geom. (2002)","DOI":"10.1007\/s00454-002-2885-2"},{"key":"16_CR10","unstructured":"Edelsbrunner, H., Harer, J.L.: Computational Topology: An Introduction. American Mathematical Society (2022)"},{"key":"16_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1007\/978-3-030-59710-8_52","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"K Fang","year":"2020","unstructured":"Fang, K., Li, W.-J.: DMNet: difference minimization network for semi-supervised segmentation in medical images. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 532\u2013541. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59710-8_52"},{"key":"16_CR12","unstructured":"Fleming, M., Ravula, S., Tatishchev, S.F., Wang, H.L.: Colorectal carcinoma: pathologic aspects. J. Gastrointest. Oncol. (2012)"},{"key":"16_CR13","first-page":"199","volume":"52","author":"S Graham","year":"2019","unstructured":"Graham, S., et al.: MILD-Net: minimal information loss dilated network for gland instance segmentation in colon histology images. MedIA 52, 199\u2013211 (2019)","journal-title":"MedIA"},{"key":"16_CR14","unstructured":"Grandvalet, Y., Bengio, Y.: Semi-supervised learning by entropy minimization. In: NeurIPS (2004)"},{"key":"16_CR15","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"701","DOI":"10.1007\/978-3-031-19818-2_40","volume-title":"ECCV 2022","author":"S Gupta","year":"2022","unstructured":"Gupta, S., et al.: Learning topological interactions for multi-class medical image segmentation. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13689, pp. 701\u2013718. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19818-2_40"},{"key":"16_CR16","unstructured":"Gupta, S., Zhang, Y., Hu, X., Prasanna, P., Chen, C.: Topology-aware uncertainty for image segmentation. In: NeurIPS (2023)"},{"key":"16_CR17","unstructured":"Hu, X.: Structure-aware image segmentation with homotopy warping. In: NeurIPS (2022)"},{"key":"16_CR18","unstructured":"Hu, X., Li, F., Samaras, D., Chen, C.: Topology-preserving deep image segmentation. In: NeurIPS (2019)"},{"key":"16_CR19","unstructured":"Hu, X., Samaras, D., Chen, C.: Learning probabilistic topological representations using discrete morse theory. In: ICLR (2023)"},{"key":"16_CR20","unstructured":"Hu, X., Wang, Y., Fuxin, L., Samaras, D., Chen, C.: Topology-aware segmentation using discrete morse theory. In: ICLR (2021)"},{"key":"16_CR21","doi-asserted-by":"crossref","unstructured":"Huang, W., et al.: Semi-supervised neuron segmentation via reinforced consistency learning. TMI (2022)","DOI":"10.1109\/TMI.2022.3176050"},{"key":"16_CR22","doi-asserted-by":"crossref","unstructured":"Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods (2021)","DOI":"10.1038\/s41592-020-01008-z"},{"key":"16_CR23","unstructured":"Jeong, J., Lee, S., Kim, J., Kwak, N.: Consistency-based semi-supervised learning for object detection. In: NeurIPS (2019)"},{"key":"16_CR24","unstructured":"Jiao, R., Zhang, Y., Ding, L., Cai, R., Zhang, J.: Learning with limited annotations: a survey on deep semi-supervised learning for medical image segmentation. arXiv preprint arXiv:2207.14191 (2022)"},{"key":"16_CR25","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-031-16434-7_1","volume-title":"MICCAI 2022","author":"Q Jin","year":"2022","unstructured":"Jin, Q., et al.: Semi-supervised histological image segmentation via hierarchical consistency enforcement. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13432, pp. 3\u201313. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16434-7_1"},{"key":"16_CR26","unstructured":"Jin, Y., Wang, J., Lin, D.: Semi-supervised semantic segmentation via gentle teaching assistant. In: NeurIPS (2022)"},{"key":"16_CR27","doi-asserted-by":"crossref","unstructured":"Kerber, M., Morozov, D., Nigmetov, A.: Geometry helps to compare persistence diagrams. In: 2016 Proceedings of the Eighteenth Workshop on Algorithm Engineering and Experiments (ALENEX). SIAM (2016)","DOI":"10.1137\/1.9781611974317.9"},{"key":"16_CR28","unstructured":"Kumar, N., et\u00a0al.: A multi-organ nucleus segmentation challenge. TMI (2019)"},{"key":"16_CR29","unstructured":"Lacombe, T., Cuturi, M., Oudot, S.: Large scale computation of means and clusters for persistence diagrams using optimal transport. In: NeurIPS (2018)"},{"key":"16_CR30","doi-asserted-by":"crossref","unstructured":"Li, C., Hu, X., Abousamra, S., Chen, C.: Calibrating uncertainty for semi-supervised crowd counting. In: ICCV (2023)","DOI":"10.1109\/ICCV51070.2023.01534"},{"key":"16_CR31","doi-asserted-by":"crossref","unstructured":"Li, X., Yu, L., Chen, H., Fu, C.W., Xing, L., Heng, P.A.: Transformation-consistent self-ensembling model for semisupervised medical image segmentation. TNNLS (2020)","DOI":"10.1109\/TNNLS.2020.2995319"},{"key":"16_CR32","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1007\/978-3-030-87196-3_19","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"Y Li","year":"2021","unstructured":"Li, Y., Luo, L., Lin, H., Chen, H., Heng, P.-A.: Dual-consistency semi-supervised learning with uncertainty quantification for COVID-19 lesion segmentation from CT images. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12902, pp. 199\u2013209. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87196-3_19"},{"key":"16_CR33","doi-asserted-by":"crossref","unstructured":"Luo, X., et al.: Semi-supervised medical image segmentation via uncertainty rectified pyramid consistency. MedIA (2022)","DOI":"10.1016\/j.media.2022.102517"},{"key":"16_CR34","doi-asserted-by":"crossref","unstructured":"Meil\u0103, M.: Comparing clusterings by the variation of information. In: Learning Theory and Kernel Machines: 16th Annual Conference on Learning Theory and 7th Kernel Workshop, COLT\/Kernel 2003 (2003)","DOI":"10.1007\/978-3-540-45167-9_14"},{"key":"16_CR35","doi-asserted-by":"crossref","unstructured":"Montironi, R., Mazzuccheli, R., Scarpelli, M., Lopez-Beltran, A., Fellegara, G., Algaba, F.: Gleason grading of prostate cancer in needle biopsies or radical prostatectomy specimens: contemporary approach, current clinical significance and sources of pathology discrepancies. BJU Int. (2005)","DOI":"10.1111\/j.1464-410X.2005.05540.x"},{"key":"16_CR36","unstructured":"Munkres, J.R.: Elements of algebraic topology (1984)"},{"key":"16_CR37","doi-asserted-by":"crossref","unstructured":"Ouali, Y., Hudelot, C., Tami, M.: Semi-supervised semantic segmentation with cross-consistency training. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01269"},{"key":"16_CR38","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":"16_CR39","doi-asserted-by":"crossref","unstructured":"Seibold, C.M., Rei\u00df, S., Kleesiek, J., Stiefelhagen, R.: Reference-guided pseudo-label generation for medical semantic segmentation. In: AAAI (2022)","DOI":"10.1609\/aaai.v36i2.20114"},{"key":"16_CR40","unstructured":"Shi, Y., et al.: Inconsistency-aware uncertainty estimation for semi-supervised medical image segmentation. TMI (2021)"},{"key":"16_CR41","doi-asserted-by":"crossref","unstructured":"Shit, S., et al.: cldice-a novel topology-preserving loss function for tubular structure segmentation. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.01629"},{"key":"16_CR42","doi-asserted-by":"crossref","unstructured":"Sirinukunwattana, K., et\u00a0al.: Gland segmentation in colon histology images: the GLAs challenge contest. MedIA (2017)","DOI":"10.1016\/j.media.2016.08.008"},{"key":"16_CR43","unstructured":"Sohn, K., et al.: Fixmatch: simplifying semi-supervised learning with consistency and confidence. In: NeurIPS (2020)"},{"key":"16_CR44","unstructured":"Stucki, N., Paetzold, J.C., Shit, S., Menze, B., Bauer, U.: Topologically faithful image segmentation via induced matching of persistence barcodes. In: ICML (2023)"},{"key":"16_CR45","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":"16_CR46","unstructured":"Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. In: NeurIPS (2017)"},{"key":"16_CR47","doi-asserted-by":"crossref","unstructured":"Thompson, B.H., Di\u00a0Caterina, G., Voisey, J.P.: Pseudo-label refinement using superpixels for semi-supervised brain tumour segmentation. In: ISBI (2022)","DOI":"10.1109\/ISBI52829.2022.9761681"},{"key":"16_CR48","doi-asserted-by":"crossref","unstructured":"Vu, T.H., Jain, H., Bucher, M., Cord, M., P\u00e9rez, P.: Advent: adversarial entropy minimization for domain adaptation in semantic segmentation. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00262"},{"key":"16_CR49","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1007\/978-3-030-58580-8_8","volume-title":"Computer Vision \u2013 ECCV 2020","author":"F Wang","year":"2020","unstructured":"Wang, F., Liu, H., Samaras, D., Chen, C.: TopoGAN: a topology-aware generative adversarial network. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12348, pp. 118\u2013136. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58580-8_8"},{"key":"16_CR50","doi-asserted-by":"crossref","unstructured":"Wang, H., Cao, P., Wang, J., Zaiane, O.R.: Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: AAAI (2022)","DOI":"10.1609\/aaai.v36i3.20144"},{"key":"16_CR51","doi-asserted-by":"crossref","unstructured":"Wang, H., Xian, M., Vakanski, A.: Ta-net: topology-aware network for gland segmentation. In: WACV (2022)","DOI":"10.1109\/WACV51458.2022.00330"},{"key":"16_CR52","doi-asserted-by":"crossref","unstructured":"Wang, X., et al.: SSA-Net: Spatial self-attention network for COVID-19 pneumonia infection segmentation with semi-supervised few-shot learning. MedIA (2022)","DOI":"10.1016\/j.media.2022.102459"},{"key":"16_CR53","doi-asserted-by":"crossref","unstructured":"Wu, H., Wang, Z., Song, Y., Yang, L., Qin, J.: Cross-patch dense contrastive learning for semi-supervised segmentation of cellular nuclei in histopathologic images. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.01137"},{"key":"16_CR54","doi-asserted-by":"crossref","unstructured":"Wu, Y., et al.: Mutual consistency learning for semi-supervised medical image segmentation. MedIA (2022)","DOI":"10.1016\/j.media.2022.102530"},{"key":"16_CR55","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1007\/978-3-030-32239-7_52","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"Y Xie","year":"2019","unstructured":"Xie, Y., Lu, H., Zhang, J., Shen, C., Xia, Y.: Deep segmentation-emendation model for gland instance segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 469\u2013477. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32239-7_52"},{"key":"16_CR56","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1007\/978-3-030-87193-2_21","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"J Yang","year":"2021","unstructured":"Yang, J., Hu, X., Chen, C., Tsai, C.: A topological-attention ConvLSTM network and its application to EM images. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 217\u2013228. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87193-2_21"},{"key":"16_CR57","doi-asserted-by":"crossref","unstructured":"Yao, H., Hu, X., Li, X.: Enhancing pseudo label quality for semi-supervised domain-generalized medical image segmentation. In: AAAI (2022)","DOI":"10.1609\/aaai.v36i3.20217"},{"key":"16_CR58","unstructured":"You, C., et al.: Rethinking semi-supervised medical image segmentation: a variance-reduction perspective. In: NeurIPS (2023)"},{"key":"16_CR59","doi-asserted-by":"crossref","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. TMI (2022)","DOI":"10.1007\/978-3-031-16440-8_61"},{"key":"16_CR60","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"},{"key":"16_CR61","doi-asserted-by":"crossref","unstructured":"Zhang, W., et al.: Boostmis: boosting medical image semi-supervised learning with adaptive pseudo labeling and informative active annotation. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.02001"},{"key":"16_CR62","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Tian, C., Bai, H.X., Jiao, Z., Tian, X.: Discriminative error prediction network for semi-supervised colon gland segmentation. MedIA (2022)","DOI":"10.1016\/j.media.2022.102458"},{"key":"16_CR63","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.660"},{"key":"16_CR64","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Huang, J., Wang, C., Song, L., Yang, G.: Xnet: wavelet-based low and high frequency fusion networks for fully-and semi-supervised semantic segmentation of biomedical images. In: ICCV (2023)","DOI":"10.1109\/ICCV51070.2023.01928"},{"key":"16_CR65","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Rahman\u00a0Siddiquee, M.M., Tajbakhsh, N., Liang, J.: Unet++: a nested u-net architecture for medical image segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018 (2018)","DOI":"10.1007\/978-3-030-00889-5_1"}],"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\/978-3-031-73229-4_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T15:08:11Z","timestamp":1729782491000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73229-4_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,25]]},"ISBN":["9783031732287","9783031732294"],"references-count":65,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73229-4_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,25]]},"assertion":[{"value":"25 October 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"}}]}}