{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,3]],"date-time":"2026-01-03T06:52:24Z","timestamp":1767423144007,"version":"3.40.3"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031439001"},{"type":"electronic","value":"9783031439018"}],"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\/978-3-031-43901-8_53","type":"book-chapter","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T23:08:23Z","timestamp":1696115303000},"page":"555-566","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Anti-adversarial Consistency Regularization for\u00a0Data Augmentation: Applications to\u00a0Robust Medical Image Segmentation"],"prefix":"10.1007","author":[{"given":"Hyuna","family":"Cho","sequence":"first","affiliation":[]},{"given":"Yubin","family":"Han","sequence":"additional","affiliation":[]},{"given":"Won Hwa","family":"Kim","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"key":"53_CR1","doi-asserted-by":"crossref","unstructured":"Alfarra, M., P\u00e9rez, J.C., et al.: Combating adversaries with anti-adversaries. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 5992\u20136000 (2022)","DOI":"10.1609\/aaai.v36i6.20545"},{"key":"53_CR2","doi-asserted-by":"crossref","unstructured":"Chaurasia, A., Culurciello, E.: Linknet: exploiting encoder representations for efficient semantic segmentation. In: 2017 IEEE Visual Communications and Image Processing, pp. 1\u20134. IEEE (2017)","DOI":"10.1109\/VCIP.2017.8305148"},{"key":"53_CR3","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., et al.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision, pp. 801\u2013818 (2018)","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"53_CR4","unstructured":"DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017)"},{"key":"53_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1007\/978-3-030-59725-2_26","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"D-P Fan","year":"2020","unstructured":"Fan, D.-P., et al.: PraNet: parallel reverse attention network for polyp segmentation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12266, pp. 263\u2013273. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59725-2_26"},{"issue":"4","key":"53_CR6","first-page":"569","volume":"32","author":"S Felder","year":"2006","unstructured":"Felder, S., Rabinovitz, H., et al.: Dermoscopic pattern of pigmented basal cell carcinoma, blue-white variant. Dermatol. Surg. 32(4), 569\u2013570 (2006)","journal-title":"Dermatol. Surg."},{"key":"53_CR7","unstructured":"Ghiasi, G., Lin, T.Y., et al.: Dropblock: a regularization method for convolutional networks. In: Advances in Neural Information Processing Systems, vol. 31 (2018)"},{"key":"53_CR8","unstructured":"Goodfellow, I.J., Shlens, J., et al.: Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)"},{"key":"53_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1007\/978-3-030-87240-3_45","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"Y Hu","year":"2021","unstructured":"Hu, Y., Zhong, Z., Wang, R., Liu, H., Tan, Z., Zheng, W.-S.: Data augmentation in logit space for medical image classification with limited training data. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12905, pp. 469\u2013479. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87240-3_45"},{"key":"53_CR10","doi-asserted-by":"crossref","unstructured":"Jha, D., Riegler, M.A., et al.: Doubleu-net: a deep convolutional neural network for medical image segmentation. In: IEEE International Symposium on Computer-Based Medical Systems, pp. 558\u2013564. IEEE (2020)","DOI":"10.1109\/CBMS49503.2020.00111"},{"key":"53_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1007\/978-3-030-37734-2_37","volume-title":"MultiMedia Modeling","author":"D Jha","year":"2020","unstructured":"Jha, D., et al.: Kvasir-SEG: a segmented polyp dataset. In: Ro, Y.M., et al. (eds.) MMM 2020. LNCS, vol. 11962, pp. 451\u2013462. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-37734-2_37"},{"key":"53_CR12","unstructured":"Juszczak, P., Duin, R.P.: Uncertainty sampling methods for one-class classifiers. In: Proceedings of ICML-03, Workshop on Learning with Imbalanced Data Sets II, pp. 81\u201388 (2003)"},{"issue":"6","key":"53_CR13","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1038\/s42256-020-0186-1","volume":"2","author":"GA Kaissis","year":"2020","unstructured":"Kaissis, G.A., Makowski, M.R., et al.: Secure, privacy-preserving and federated machine learning in medical imaging. Nat. Mach. Intell. 2(6), 305\u2013311 (2020)","journal-title":"Nat. Mach. Intell."},{"issue":"1","key":"53_CR14","doi-asserted-by":"publisher","first-page":"8379","DOI":"10.1038\/s41598-020-65387-1","volume":"10","author":"JY Lee","year":"2020","unstructured":"Lee, J.Y., Jeong, J., et al.: Real-time detection of colon polyps during colonoscopy using deep learning: systematic validation with four independent datasets. Sci. Rep. 10(1), 8379 (2020)","journal-title":"Sci. Rep."},{"key":"53_CR15","doi-asserted-by":"crossref","unstructured":"Lee, J., Kim, E., et al.: Anti-adversarially manipulated attributions for weakly and semi-supervised semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4071\u20134080 (2021)","DOI":"10.1109\/CVPR46437.2021.00406"},{"key":"53_CR16","doi-asserted-by":"crossref","unstructured":"Lewis, D.D., Catlett, J.: Heterogeneous uncertainty sampling for supervised learning. In: Machine Learning Proceedings 1994, pp. 148\u2013156. Elsevier (1994)","DOI":"10.1016\/B978-1-55860-335-6.50026-X"},{"key":"53_CR17","unstructured":"Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(11) (2008)"},{"key":"53_CR18","unstructured":"Madry, A., Makelov, A., et al.: Towards deep learning models resistant to adversarial attacks. arXiv preprint arXiv:1706.06083 (2017)"},{"issue":"1","key":"53_CR19","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1007\/s10994-021-06003-9","volume":"111","author":"VL Nguyen","year":"2022","unstructured":"Nguyen, V.L., Shaker, M.H., et al.: How to measure uncertainty in uncertainty sampling for active learning. Mach. Learn. 111(1), 89\u2013122 (2022)","journal-title":"Mach. Learn."},{"key":"53_CR20","doi-asserted-by":"crossref","unstructured":"Parmar, B., Talati, B.: Automated melanoma types and stages classification for dermoscopy images. In: 2019 Innovations in Power and Advanced Computing Technologies, vol. 1, pp. 1\u20137. IEEE (2019)","DOI":"10.1109\/i-PACT44901.2019.8960137"},{"key":"53_CR21","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":"53_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1007\/978-3-030-58580-8_4","volume-title":"Computer Vision \u2013 ECCV 2020","author":"E Rusak","year":"2020","unstructured":"Rusak, E., et al.: A simple way to make neural networks robust against diverse image corruptions. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12348, pp. 53\u201369. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58580-8_4"},{"key":"53_CR23","doi-asserted-by":"crossref","unstructured":"Saleh, F.S., Aliakbarian, M.S., et al.: Effective use of synthetic data for urban scene semantic segmentation. In: Proceedings of the European Conference on Computer Vision, pp. 84\u2013100 (2018)","DOI":"10.1007\/978-3-030-01216-8_6"},{"key":"53_CR24","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"892","DOI":"10.1007\/978-3-031-12053-4_65","volume-title":"Medical Image Understanding and Analysis","author":"E Sanderson","year":"2022","unstructured":"Sanderson, E., Matuszewski, B.J.: FCN-transformer feature fusion for polyp segmentation. In: Yang, G., Aviles-Rivero, A., Roberts, M., Sch\u00f6nlieb, C.B. (eds.) MIUA 2022. LNCS, vol. 13413, pp. 892\u2013907. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-12053-4_65"},{"key":"53_CR25","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1007\/s11548-013-0926-3","volume":"9","author":"J Silva","year":"2014","unstructured":"Silva, J., Histace, A., et al.: Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer. Int. J. Comput. Assist. Radiol. Surg. 9, 283\u2013293 (2014)","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"53_CR26","unstructured":"Simard, P.Y., Steinkraus, D., et al.: Best practices for convolutional neural networks applied to visual document analysis. In: International Conference on Document Analysis and Recognition, vol. 3 (2003)"},{"issue":"5","key":"53_CR27","doi-asserted-by":"publisher","first-page":"2252","DOI":"10.1109\/JBHI.2021.3138024","volume":"26","author":"A Srivastava","year":"2021","unstructured":"Srivastava, A., Jha, D., et al.: MSRF-Net: a multi-scale residual fusion network for biomedical image segmentation. IEEE J. Biomed. Health Inform. 26(5), 2252\u20132263 (2021)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"53_CR28","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":"53_CR29","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1007\/978-3-031-16437-8_11","volume-title":"Medical Image Computing and Computer Assisted Intervention","author":"J Wang","year":"2022","unstructured":"Wang, J., Huang, Q., et al.: Stepwise feature fusion: Local guides global. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13433, pp. 110\u2013120. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16437-8_11"},{"key":"53_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"579","DOI":"10.1007\/978-3-030-87193-2_55","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"J Yang","year":"2021","unstructured":"Yang, J., Zhang, Y., Liang, Y., Zhang, Y., He, L., He, Z.: TumorCP: a simple but effective object-level data augmentation for tumor segmentation. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 579\u2013588. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87193-2_55"},{"key":"53_CR31","doi-asserted-by":"crossref","unstructured":"Yun, S., Han, D., et al.: Cutmix: regularization strategy to train strong classifiers with localizable features. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6023\u20136032 (2019)","DOI":"10.1109\/ICCV.2019.00612"},{"key":"53_CR32","doi-asserted-by":"crossref","unstructured":"Zhao, X., Vemulapalli, R., et al.: Contrastive learning for label efficient semantic segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10623\u201310633 (2021)","DOI":"10.1109\/ICCV48922.2021.01045"},{"key":"53_CR33","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Zheng, L., et al.: Random erasing data augmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 13001\u201313008 (2020)","DOI":"10.1609\/aaai.v34i07.7000"},{"key":"53_CR34","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-00889-5_1","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"Z Zhou","year":"2018","unstructured":"Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: UNet++: a nested U-net architecture for medical image segmentation. In: Stoyanov, D., et al. (eds.) DLMIA\/ML-CDS -2018. LNCS, vol. 11045, pp. 3\u201311. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00889-5_1"},{"issue":"1","key":"53_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-021-93030-0","volume":"11","author":"A Ziller","year":"2021","unstructured":"Ziller, A., Usynin, D., et al.: Medical imaging deep learning with differential privacy. Sci. Rep. 11(1), 1\u20138 (2021)","journal-title":"Sci. Rep."}],"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\/978-3-031-43901-8_53","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T15:24:27Z","timestamp":1710170667000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43901-8_53"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031439001","9783031439018"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43901-8_53","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"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)"}}]}}