{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T16:35:17Z","timestamp":1780763717685,"version":"3.54.1"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031164514","type":"print"},{"value":"9783031164521","type":"electronic"}],"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.springernature.com\/gp\/researchers\/text-and-data-mining"},{"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.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-16452-1_30","type":"book-chapter","created":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T21:25:46Z","timestamp":1663277146000},"page":"309-319","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Intervention & Interaction Federated Abnormality Detection with\u00a0Noisy Clients"],"prefix":"10.1007","author":[{"given":"Xinyu","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wuyang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yixuan","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,9,16]]},"reference":[{"key":"30_CR1","unstructured":"Ali, S., et al.: PolypGen: a multi-center polyp detection and segmentation dataset for generalisability assessment. arXiv preprint arXiv:2106.04463 (2021)"},{"key":"30_CR2","doi-asserted-by":"publisher","unstructured":"Bernal, J., Histace, A.: Computer-Aided Analysis of Gastrointestinal Videos. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-64340-9","DOI":"10.1007\/978-3-030-64340-9"},{"key":"30_CR3","unstructured":"Deng, X., Zhang, Z.: Comprehensive knowledge distillation with causal intervention. In: NeurIPS, vol. 34 (2021)"},{"issue":"2","key":"30_CR4","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vis. 88(2), 303\u2013338 (2010)","journal-title":"Int. J. Comput. Vis."},{"key":"30_CR5","doi-asserted-by":"crossref","unstructured":"Ghosh, A., Kumar, H., Sastry, P.: Robust loss functions under label noise for deep neural networks. In: AAAI, vol. 31 (2017)","DOI":"10.1609\/aaai.v31i1.10894"},{"key":"30_CR6","unstructured":"Han, B., et al.: Co-teaching: robust training of deep neural networks with extremely noisy labels. In: NeurIPS, vol. 31 (2018)"},{"key":"30_CR7","doi-asserted-by":"crossref","unstructured":"Hu, X., Tang, K., Miao, C., Hua, X.S., Zhang, H.: Distilling causal effect of data in class-incremental learning. In: CVPR, pp. 3957\u20133966 (2021)","DOI":"10.1109\/CVPR46437.2021.00395"},{"issue":"3","key":"30_CR8","first-page":"1570","volume":"17","author":"X Jia","year":"2020","unstructured":"Jia, X., et al.: Automatic polyp recognition in colonoscopy images using deep learning and two-stage pyramidal feature prediction. IEEE Trans. Autom. Sci. Eng. 17(3), 1570\u20131584 (2020)","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"30_CR9","unstructured":"Jiang, L., Zhou, Z., Leung, T., Li, L.J., Fei-Fei, L.: MentorNet: learning data-driven curriculum for very deep neural networks on corrupted labels. In: ICML, pp. 2304\u20132313. PMLR (2018)"},{"issue":"8","key":"30_CR10","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0255809","volume":"16","author":"K Li","year":"2021","unstructured":"Li, K., et al.: Colonoscopy polyp detection and classification: dataset creation and comparative evaluations. PLoS ONE 16(8), e0255809 (2021)","journal-title":"PLoS ONE"},{"key":"30_CR11","unstructured":"Liu, S., Niles-Weed, J., Razavian, N., Fernandez-Granda, C.: Early-learning regularization prevents memorization of noisy labels. In: NeurIPS, vol. 33, pp. 20331\u201320342 (2020)"},{"key":"30_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102052","volume":"71","author":"X Liu","year":"2021","unstructured":"Liu, X., Guo, X., Liu, Y., Yuan, Y.: Consolidated domain adaptive detection and localization framework for cross-device colonoscopic images. Med. Image Anal. 71, 102052 (2021)","journal-title":"Med. Image Anal."},{"key":"30_CR13","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: AISTATS, pp. 1273\u20131282. PMLR (2017)"},{"issue":"9","key":"30_CR14","doi-asserted-by":"publisher","first-page":"2051","DOI":"10.1109\/TMI.2016.2547947","volume":"35","author":"P Mesejo","year":"2016","unstructured":"Mesejo, P., et al.: Computer-aided classification of gastrointestinal lesions in regular colonoscopy. IEEE Trans. Med. Imag. 35(9), 2051\u20132063 (2016)","journal-title":"IEEE Trans. Med. Imag."},{"key":"30_CR15","unstructured":"Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. arXiv preprint arXiv:1912.01703 (2019)"},{"key":"30_CR16","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1214\/09-SS057","volume":"3","author":"J Pearl","year":"2009","unstructured":"Pearl, J.: Causal inference in statistics: an overview. Stat. Surv. 3, 96\u2013146 (2009)","journal-title":"Stat. Surv."},{"key":"30_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"786","DOI":"10.1007\/978-3-030-58517-4_46","volume-title":"Computer Vision \u2013 ECCV 2020","author":"X Peng","year":"2020","unstructured":"Peng, X., Wang, K., Zeng, Z., Li, Q., Yang, J., Qiao, Yu.: Suppressing mislabeled data via grouping and self-attention. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12361, pp. 786\u2013802. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58517-4_46"},{"key":"30_CR18","unstructured":"Pereyra, G., Tucker, G., Chorowski, J., Kaiser, \u0141., Hinton, G.: Regularizing neural networks by penalizing confident output distributions. arXiv preprint arXiv:1701.06548 (2017)"},{"key":"30_CR19","doi-asserted-by":"publisher","first-page":"40950","DOI":"10.1109\/ACCESS.2018.2856402","volume":"6","author":"Y Shin","year":"2018","unstructured":"Shin, Y., Qadir, H.A., Aabakken, L., Bergsland, J., Balasingham, I.: Automatic colon polyp detection using region based deep CNN and post learning approaches. IEEE Access 6, 40950\u201340962 (2018)","journal-title":"IEEE Access"},{"key":"30_CR20","doi-asserted-by":"crossref","unstructured":"Tajbakhsh, N., Gurudu, S.R., Liang, J.: Automatic polyp detection in colonoscopy videos using an ensemble of convolutional neural networks. In: ISBI, pp. 79\u201383 (2015)","DOI":"10.1109\/ISBI.2015.7163821"},{"key":"30_CR21","doi-asserted-by":"crossref","unstructured":"Tian, Z., Shen, C., Chen, H., He, T.: FCOS: fully convolutional one-stage object detection. In: ICCV, pp. 9627\u20139636 (2019)","DOI":"10.1109\/ICCV.2019.00972"},{"key":"30_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1007\/978-3-030-68793-9_23","volume-title":"Pattern Recognition. ICPR International Workshops and Challenges","author":"NK Tomar","year":"2021","unstructured":"Tomar, N.K., et al.: DDANet: dual decoder attention network for automatic polyp segmentation. In: Del Bimbo, A., et al. (eds.) ICPR 2021. LNCS, vol. 12668, pp. 307\u2013314. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-68793-9_23"},{"key":"30_CR23","unstructured":"Verma, V., et al.: Manifold mixup: better representations by interpolating hidden states. In: ICML, pp. 6438\u20136447. PMLR (2019)"},{"key":"30_CR24","doi-asserted-by":"crossref","unstructured":"Wang, D., et al.: AFP-Net: realtime anchor-free polyp detection in colonoscopy. In: ICTAI, pp. 636\u2013643 (2019)","DOI":"10.1109\/ICTAI.2019.00094"},{"key":"30_CR25","doi-asserted-by":"crossref","unstructured":"Wang, Y., Ma, X., Chen, Z., Luo, Y., Yi, J., Bailey, J.: Symmetric cross entropy for robust learning with noisy labels. In: ICCV, pp. 322\u2013330 (2019)","DOI":"10.1109\/ICCV.2019.00041"},{"key":"30_CR26","unstructured":"Xia, X., et al.: Robust early-learning: hindering the memorization of noisy labels. In: ICLR (2020)"},{"key":"30_CR27","unstructured":"Xu, K., et al.: Show, attend and tell: neural image caption generation with visual attention. In: ICML, pp. 2048\u20132057. PMLR (2015)"},{"key":"30_CR28","doi-asserted-by":"crossref","unstructured":"Yang, Q., Zhang, J., Hao, W., Spell, G.P., Carin, L.: FLOP: federated learning on medical datasets using partial networks. In: KDD, pp. 3845\u20133853 (2021)","DOI":"10.1145\/3447548.3467185"},{"key":"30_CR29","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Meng, M.Q.H.: Automatic bleeding frame detection in the wireless capsule endoscopy images. In: ICRA, pp. 1310\u20131315 (2015)","DOI":"10.1109\/ICRA.2015.7139360"},{"key":"30_CR30","unstructured":"Yue, Z., Zhang, H., Sun, Q., Hua, X.S.: Interventional few-shot learning. In: NeurIPS, vol. 33 (2020)"},{"issue":"3","key":"30_CR31","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1145\/3446776","volume":"64","author":"C Zhang","year":"2021","unstructured":"Zhang, C., Bengio, S., Hardt, M., Recht, B., Vinyals, O.: Understanding deep learning (still) requires rethinking generalization. Commun. ACM 64(3), 107\u2013115 (2021)","journal-title":"Commun. ACM"},{"key":"30_CR32","unstructured":"Zhang, D., Zhang, H., Tang, J., Hua, X.S., Sun, Q.: Causal intervention for weakly-supervised semantic segmentation. In: NeurIPS, vol. 33 (2020)"},{"key":"30_CR33","unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: Mixup: beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017)"},{"key":"30_CR34","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1016\/j.patcog.2018.05.026","volume":"83","author":"R Zhang","year":"2018","unstructured":"Zhang, R., Zheng, Y., Poon, C.C., Shen, D., Lau, J.Y.: Polyp detection during colonoscopy using a regression-based convolutional neural network with a tracker. Pattern Recognit. 83, 209\u2013219 (2018)","journal-title":"Pattern Recognit."},{"key":"30_CR35","doi-asserted-by":"crossref","unstructured":"Zhang, W., Wang, Y., Qiao, Y.: MetaCleaner: learning to hallucinate clean representations for noisy-labeled visual recognition. In: CVPR, pp. 7373\u20137382 (2019)","DOI":"10.1109\/CVPR.2019.00755"},{"key":"30_CR36","unstructured":"Zhang, Z., Sabuncu, M.R.: Generalized cross entropy loss for training deep neural networks with noisy labels. In: NeurIPS (2018)"},{"key":"30_CR37","doi-asserted-by":"crossref","unstructured":"Zhou, X., Liu, X., Wang, C., Zhai, D., Jiang, J., Ji, X.: Learning with noisy labels via sparse regularization. In: ICCV, pp. 72\u201381 (2021)","DOI":"10.1109\/ICCV48922.2021.00014"},{"key":"30_CR38","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Shin, J., Zhang, L., Gurudu, S., Gotway, M., Liang, J.: Fine-tuning convolutional neural networks for biomedical image analysis: actively and incrementally. In: CVPR, pp. 7340\u20137351 (2017)","DOI":"10.1109\/CVPR.2017.506"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16452-1_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T11:47:03Z","timestamp":1710244023000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16452-1_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164514","9783031164521"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16452-1_30","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"16 September 2022","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":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","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":"18 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2022","order":10,"name":"conference_id","label":"Conference ID","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":"Microsoft Conference","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1831","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":"574","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":"31% - 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)"}}]}}