{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T16:20:16Z","timestamp":1775665216146,"version":"3.50.1"},"publisher-location":"Cham","reference-count":39,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030877217","type":"print"},{"value":"9783030877224","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-87722-4_2","type":"book-chapter","created":{"date-parts":[[2021,9,24]],"date-time":"2021-09-24T01:17:40Z","timestamp":1632446260000},"page":"14-24","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Self-supervised Multi-scale Consistency for Weakly Supervised Segmentation Learning"],"prefix":"10.1007","author":[{"given":"Gabriele","family":"Valvano","sequence":"first","affiliation":[]},{"given":"Andrea","family":"Leo","sequence":"additional","affiliation":[]},{"given":"Sotirios A.","family":"Tsaftaris","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,21]]},"reference":[{"key":"2_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1007\/978-3-030-32245-8_60","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"W Bai","year":"2019","unstructured":"Bai, W., et al.: Self-supervised learning for cardiac MR image segmentation by anatomical position prediction. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 541\u2013549. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_60"},{"key":"2_CR2","unstructured":"Belharbi, S., Rony, J., Dolz, J., Ayed, I.B., McCaffrey, L., Granger, E.: Deep interpretable classification and weakly-supervised segmentation of histology images via max-min uncertainty. arXiv preprint arXiv:2011.07221 (2020)"},{"key":"2_CR3","doi-asserted-by":"crossref","unstructured":"Bernard, O.E.A.: Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE TMI (2018)","DOI":"10.1109\/TMI.2018.2837502"},{"key":"2_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1007\/978-3-030-00889-5_27","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"YB Can","year":"2018","unstructured":"Can, Y.B., Chaitanya, K., Mustafa, B., Koch, L.M., Konukoglu, E., Baumgartner, C.F.: Learning to segment medical images with scribble-supervision alone. In: Stoyanov, D., et al. (eds.) DLMIA\/ML-CDS-2018. LNCS, vol. 11045, pp. 236\u2013244. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00889-5_27"},{"key":"2_CR5","unstructured":"Chaitanya, K., Erdil, E., Karani, N., Konukoglu, E.: Contrastive learning of global and local features for medical image segmentation with limited annotations. NeurIPS 33 (2020)"},{"key":"2_CR6","doi-asserted-by":"crossref","unstructured":"Chen, L., Bentley, P., Mori, K., Misawa, K., Fujiwara, M., Rueckert, D.: Self-supervised learning for medical image analysis using image context restoration. MIA 58, 101539 (2019)","DOI":"10.1016\/j.media.2019.101539"},{"issue":"4","key":"2_CR7","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"LC Chen","year":"2017","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE TPAMI 40(4), 834\u2013848 (2017)","journal-title":"IEEE TPAMI"},{"key":"2_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1007\/978-3-030-59710-8_47","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"R Dorent","year":"2020","unstructured":"Dorent, R., et al.: Scribble-based domain adaptation via co-segmentation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 479\u2013489. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59710-8_47"},{"key":"2_CR9","first-page":"40","volume":"41","author":"Q Dou","year":"2017","unstructured":"Dou, Q., et al.: 3D deeply supervised network for automated segmentation of volumetric medical images. MIA 41, 40\u201354 (2017)","journal-title":"MIA"},{"key":"2_CR10","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning (ICML), pp. 448\u2013456. PMLR (2015)"},{"key":"2_CR11","unstructured":"Jetley, S., Lord, N.A., Lee, N., Torr, P.H.S.: Learn to pay attention. ICLR (2018)"},{"key":"2_CR12","unstructured":"Kavur, A.E., Selver, M.A., Dicle, O., Bari\u015f, M., Gezer, N.S.: CHAOS - Combined (CT-MR) Healthy Abdominal Organ Segmentation Challenge Data, April 2019"},{"key":"2_CR13","unstructured":"Kayhan, O.S., Gemert, J.C.v.: On translation invariance in CNNs: convolutional layers can exploit absolute spatial location. In: CVPR, pp. 14274\u201314285 (2020)"},{"key":"2_CR14","first-page":"88","volume":"54","author":"H Kervadec","year":"2019","unstructured":"Kervadec, H., Dolz, J., Tang, M., Granger, E., Boykov, Y., Ayed, I.B.: Constrained-CNN losses for weakly supervised segmentation. MIA 54, 88\u201399 (2019)","journal-title":"MIA"},{"key":"2_CR15","doi-asserted-by":"crossref","unstructured":"Kervadec, H., Dolz, J., Wang, S., Granger, E., Ayed, I.B.: Bounding boxes for weakly supervised segmentation: global constraints get close to full supervision. In: MIDL (2020)","DOI":"10.1016\/j.media.2019.02.009"},{"key":"2_CR16","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2015)"},{"key":"2_CR17","doi-asserted-by":"crossref","unstructured":"Lin, D., Dai, J., Jia, J., He, K., Sun, J.: ScribbleSup: scribble-supervised convolutional networks for semantic segmentation. In: CVPR, pp. 3159\u20133167 (2016)","DOI":"10.1109\/CVPR.2016.344"},{"key":"2_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1007\/978-3-030-59713-9_46","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"Q Liu","year":"2020","unstructured":"Liu, Q., Dou, Q., Heng, P.-A.: Shape-aware meta-learning for generalizing prostate MRI segmentation to unseen domains. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12262, pp. 475\u2013485. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59713-9_46"},{"key":"2_CR19","doi-asserted-by":"crossref","unstructured":"Luo, P., Wang, X., Tang, X.: Pedestrian parsing via deep decompositional network. In: ICCV, pp. 2648\u20132655 (2013)","DOI":"10.1109\/ICCV.2013.329"},{"key":"2_CR20","unstructured":"Nosrati, M.S., Hamarneh, G.: Incorporating prior knowledge in medical image segmentation: a survey. arXiv preprint arXiv:1607.01092 (2016)"},{"key":"2_CR21","unstructured":"Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., et al.: Attention U-net: learning where to look for the pancreas. In: MIDL (2018)"},{"key":"2_CR22","unstructured":"Ouali, Y., Hudelot, C., Tami, M.: An overview of deep semi-supervised learning. arXiv preprint arXiv:2006.052 78 (2020)"},{"key":"2_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"762","DOI":"10.1007\/978-3-030-58526-6_45","volume-title":"Computer Vision \u2013 ECCV 2020","author":"C Ouyang","year":"2020","unstructured":"Ouyang, C., Biffi, C., Chen, C., Kart, T., Qiu, H., Rueckert, D.: Self-supervision with superpixels: training few-shot medical image segmentation without annotation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12374, pp. 762\u2013780. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58526-6_45"},{"key":"2_CR24","doi-asserted-by":"crossref","unstructured":"Patel, G., Dolz, J.: Weakly supervised segmentation with cross-modality equivariant constraints. arXiv preprint arXiv: 2104.02488 (2021)","DOI":"10.1016\/j.media.2022.102374"},{"issue":"11","key":"2_CR25","first-page":"3655","volume":"39","author":"H Qu","year":"2020","unstructured":"Qu, H., et al.: Weakly supervised deep nuclei segmentation using partial points annotation in histopathology images. IEEE TMI 39(11), 3655\u20133666 (2020)","journal-title":"IEEE TMI"},{"key":"2_CR26","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":"2_CR27","first-page":"197","volume":"53","author":"J Schlemper","year":"2019","unstructured":"Schlemper, J., et al.: Attention gated networks: learning to leverage salient regions in medical images. MIA 53, 197\u2013207 (2019)","journal-title":"MIA"},{"key":"2_CR28","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1109\/JBHI.2020.2986926","volume":"25","author":"A Sinha","year":"2020","unstructured":"Sinha, A., Dolz, J.: Multi-scale self-guided attention for medical image segmentation. IEEE J. Biomed. Health Inform. 25, 121\u2013130 (2020)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"1","key":"2_CR29","first-page":"50","volume":"18","author":"A Suinesiaputra","year":"2014","unstructured":"Suinesiaputra, A., et al.: A collaborative resource to build consensus for automated left ventricular segmentation of cardiac MR images. MIA 18(1), 50\u201362 (2014)","journal-title":"MIA"},{"key":"2_CR30","doi-asserted-by":"crossref","unstructured":"Tang, M., Djelouah, A., Perazzi, F., Boykov, Y., Schroers, C.: Normalized cut loss for weakly-supervised CNN segmentation. In: CVPR, pp. 1818\u20131827 (2018)","DOI":"10.1109\/CVPR.2018.00195"},{"key":"2_CR31","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1007\/978-3-030-33391-1_2","volume-title":"Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data","author":"G Valvano","year":"2019","unstructured":"Valvano, G., Chartsias, A., Leo, A., Tsaftaris, S.A.: Temporal consistency objectives regularize the learning of disentangled representations. In: Wang, Q., et al. (eds.) DART\/MIL3ID 2019. LNCS, vol. 11795, pp. 11\u201319. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-33391-1_2"},{"key":"2_CR32","doi-asserted-by":"crossref","unstructured":"Valvano, G., Leo, A., Tsaftaris, S.A.: Learning to segment from scribbles using multi-scale adversarial attention gates. IEEE TMI (2021)","DOI":"10.1109\/TMI.2021.3069634"},{"key":"2_CR33","unstructured":"Xie, Y., Zhang, J., Liao, Z., Xia, Y., Shen, C.: PGL: prior-guided local self-supervised learning for 3d medical image segmentation. arXiv preprint arXiv: 2011.12640 (2020)"},{"key":"2_CR34","doi-asserted-by":"crossref","unstructured":"Yi, X., Walia, E., Babyn, P.: Generative adversarial network in medical imaging: a review. MIA 58, 101552 (2019)","DOI":"10.1016\/j.media.2019.101552"},{"key":"2_CR35","doi-asserted-by":"crossref","unstructured":"Zamir, A.R., Sax, A., Shen, W., Guibas, L.J., Malik, J., Savarese, S.: Taskonomy: disentangling Task Transfer Learning. In: CVPR, pp. 3712\u20133722 (2018)","DOI":"10.24963\/ijcai.2019\/871"},{"key":"2_CR36","unstructured":"Zhang, H., Goodfellow, I., Metaxas, D., Odena, A.: Self-attention generative adversarial networks. In: ICLR, pp. 7354\u20137363. PMLR (2019)"},{"key":"2_CR37","unstructured":"Zhang, P., Zhong, Y., Li, X.: ACCL: adversarial constrained-CNN loss for weakly supervised medical image segmentation. arXiv:2005.00328 (2020)"},{"key":"2_CR38","doi-asserted-by":"crossref","unstructured":"Zhou, Y., et al.: Prior-aware neural network for partially-supervised multi-organ segmentation. In: ICCV, pp. 10672\u201310681 (2019)","DOI":"10.1109\/ICCV.2019.01077"},{"key":"2_CR39","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1007\/978-3-030-32251-9_42","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"Z Zhou","year":"2019","unstructured":"Zhou, Z., et al.: Models genesis: generic autodidactic models for 3D medical image analysis. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11767, pp. 384\u2013393. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32251-9_42"}],"container-title":["Lecture Notes in Computer Science","Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87722-4_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,6]],"date-time":"2023-04-06T08:19:31Z","timestamp":1680769171000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87722-4_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030877217","9783030877224"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87722-4_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"21 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DART","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"MICCAI Workshop on Domain Adaptation and Representation Transfer","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Strasbourg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dart2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/dart2021\/home","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":"21","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":"13","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":"62% - 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":"3.05","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":"2","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)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}