{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T06:46:06Z","timestamp":1747118766819,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031120527"},{"type":"electronic","value":"9783031120534"}],"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\/978-3-031-12053-4_22","type":"book-chapter","created":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T09:15:50Z","timestamp":1658740550000},"page":"283-297","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Rotation-Equivariant Semantic Instance Segmentation on\u00a0Biomedical Images"],"prefix":"10.1007","author":[{"given":"Karl Bengtsson","family":"Bernander","sequence":"first","affiliation":[]},{"given":"Joakim","family":"Lindblad","sequence":"additional","affiliation":[]},{"given":"Robin","family":"Strand","sequence":"additional","affiliation":[]},{"given":"Ingela","family":"Nystr\u00f6m","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,7,25]]},"reference":[{"key":"22_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1007\/978-3-030-71278-5_24","volume-title":"Pattern Recognition","author":"A Bailoni","year":"2021","unstructured":"Bailoni, A., Pape, C., Wolf, S., Kreshuk, A., Hamprecht, F.A.: Proposal-free volumetric instance segmentation from latent single-instance masks. In: Akata, Z., Geiger, A., Sattler, T. (eds.) DAGM GCPR 2020. LNCS, vol. 12544, pp. 331\u2013344. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-71278-5_24"},{"key":"22_CR2","doi-asserted-by":"crossref","unstructured":"Batzner, S., et al.: E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. arXiv (2021)","DOI":"10.21203\/rs.3.rs-244137\/v1"},{"key":"22_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1007\/978-3-030-93420-0_3","volume-title":"Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications","author":"KB Bernander","year":"2021","unstructured":"Bernander, K.B., Lindblad, J., Strand, R., Nystr\u00f6m, I.: Replacing data augmentation with\u00a0rotation-equivariant CNNs in\u00a0image-based classification of\u00a0oral cancer. In: Tavares, J.M.R.S., Papa, J.P., Gonz\u00e1lez Hidalgo, M. (eds.) CIARP 2021. LNCS, vol. 12702, pp. 24\u201333. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-93420-0_3"},{"key":"22_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"810","DOI":"10.1007\/978-3-030-32226-7_90","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"G Bortsova","year":"2019","unstructured":"Bortsova, G., Dubost, F., Hogeweg, L., Katramados, I., de Bruijne, M.: Semi-supervised medical image segmentation via learning consistency under transformations. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 810\u2013818. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32226-7_90"},{"key":"22_CR5","doi-asserted-by":"crossref","unstructured":"Brabandere, B.D., Neven, D., Gool, L.V.: Semantic instance segmentation with a discriminative loss function. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2017)","DOI":"10.1109\/CVPRW.2017.66"},{"key":"22_CR6","doi-asserted-by":"crossref","unstructured":"Caicedo, J.C., et al.: Nucleus segmentation across imaging experiments: the: data science bowl. Nature Methods 16, 1247\u20131253 (2019)","DOI":"10.1038\/s41592-019-0612-7"},{"key":"22_CR7","doi-asserted-by":"publisher","unstructured":"Chidester, B., Ton, T., Tran, M., Ma, J., Do, M.N.: Enhanced rotation-equivariant U-Net for nuclear segmentation. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1097\u20131104 (2019). https:\/\/doi.org\/10.1109\/CVPRW.2019.00143","DOI":"10.1109\/CVPRW.2019.00143"},{"key":"22_CR8","unstructured":"Cohen, T.S., Welling, M.: Group equivariant convolutional networks. In: Proceedings of the 33rd International Conference on International Conference on Machine Learning, ICML 2016, pp. 2990\u20132999. JMLR.org (2016)"},{"key":"22_CR9","doi-asserted-by":"crossref","unstructured":"Feng, Z., Xu, C., Tao, D.: Self-supervised representation learning by rotation feature decoupling. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019","DOI":"10.1109\/CVPR.2019.01061"},{"key":"22_CR10","doi-asserted-by":"publisher","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 580\u2013587 (2014). https:\/\/doi.org\/10.1109\/CVPR.2014.81","DOI":"10.1109\/CVPR.2014.81"},{"issue":"3","key":"22_CR11","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1007\/s13735-020-00195-x","volume":"9","author":"AM Hafiz","year":"2020","unstructured":"Hafiz, A.M., Bhat, G.M.: A survey on instance segmentation: state of the art. Int. J. Multimedia Inf. Retrieval 9(3), 171\u2013189 (2020). https:\/\/doi.org\/10.1007\/s13735-020-00195-x","journal-title":"Int. J. Multimedia Inf. Retrieval"},{"key":"22_CR12","doi-asserted-by":"crossref","unstructured":"Han, J., Ding, J., Xue, N., Xia, G.-S.: ReDet: a rotation-equivariant detector for aerial object detection. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2786\u20132795 (2021)","DOI":"10.1109\/CVPR46437.2021.00281"},{"key":"22_CR13","doi-asserted-by":"publisher","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN. In: IEEE International Conference on Computer Vision (ICCV), pp. 2980\u20132988 (2017). https:\/\/doi.org\/10.1109\/ICCV.2017.322","DOI":"10.1109\/ICCV.2017.322"},{"key":"22_CR14","unstructured":"Hestness, J., et al.: Deep learning scaling is predictable, empirically. arXiv (2017)"},{"key":"22_CR15","doi-asserted-by":"publisher","unstructured":"J\u00e9gou, S., Drozdzal, M., Vazquez, D., Romero, A., Bengio, Y.: The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1175\u20131183 (2017). https:\/\/doi.org\/10.1109\/CVPRW.2017.156","DOI":"10.1109\/CVPRW.2017.156"},{"key":"22_CR16","doi-asserted-by":"publisher","unstructured":"Laradji, I., et al.: A weakly supervised consistency-based learning method for covid-19 segmentation in CT images. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2452\u20132461 (2021). https:\/\/doi.org\/10.1109\/WACV48630.2021.00250","DOI":"10.1109\/WACV48630.2021.00250"},{"key":"22_CR17","unstructured":"Pielawski, N., et al.: CoMIR: contrastive multimodal image representation for registration. In: Advances in Neural Information Processing Systems, vol. 33, pages 18433\u201318444. Curran Associates Inc (2020). https:\/\/proceedings.neurips.cc\/paper\/2020\/file\/d6428eecbe0f7dff83fc607c5044b2b9-Paper.pdf"},{"key":"22_CR18","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":"22_CR19","doi-asserted-by":"publisher","unstructured":"Ro\u00df, T., et al.: Comparative validation of multi-instance instrument segmentation in endoscopy: Results of the robust-mis: 2019 challenge. Med. Image Anal. 70, 101920 (2021). https:\/\/doi.org\/10.1016\/j.media.2020.101920","DOI":"10.1016\/j.media.2020.101920"},{"issue":"1","key":"22_CR20","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1038\/s41592-020-01018-x","volume":"18","author":"C Stinger","year":"2020","unstructured":"Stinger, C., Wang, T., Michaelos, M., Pachitariu, M.: Cellpose: a generalist algorithm for cellular segmentation. Nat. Methods 18(1), 100\u2013106 (2020)","journal-title":"Nat. Methods"},{"key":"22_CR21","unstructured":"Taniai, H.: pytorch-discriminative-loss (2018). https:\/\/github.com\/nyoki-mtl\/pytorch-discriminative-loss. Accessed 15 Nov 2021"},{"key":"22_CR22","unstructured":"Weiler, M., Cesa, G.: General E(2)-equivariant steerable CNNs. In: Advances in Neural Information Processing Systems, vol. 32. Curran Associates Inc (2019). https:\/\/proceedings.neurips.cc\/paper\/2019\/file\/45d6637b718d0f24a237069fe41b0db4-Paper.pdf"}],"container-title":["Lecture Notes in Computer Science","Medical Image Understanding and Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-12053-4_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,8]],"date-time":"2024-02-08T08:06:19Z","timestamp":1707379579000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-12053-4_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031120527","9783031120534"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-12053-4_22","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":"25 July 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MIUA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Annual Conference on Medical Image Understanding and Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cambridge","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 July 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 July 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miua2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.miua2022.com\/home","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}