{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T05:25:51Z","timestamp":1773811551849,"version":"3.50.1"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031439865","type":"print"},{"value":"9783031439872","type":"electronic"}],"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\/978-3-031-43987-2_30","type":"book-chapter","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T23:07:48Z","timestamp":1696115268000},"page":"306-316","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Learning Robust Classifier for\u00a0Imbalanced Medical Image Dataset with\u00a0Noisy Labels by\u00a0Minimizing Invariant Risk"],"prefix":"10.1007","author":[{"given":"Jinpeng","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanqun","family":"Cao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaze","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Furui","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Dou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangyong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pheng-Ann","family":"Heng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"key":"30_CR1","unstructured":"Arazo, E., Ortego, D., Albert, P., O\u2019Connor, N.E., McGuinness, K.: Unsupervised label noise modeling and loss correction. In: Chaudhuri, K., Salakhutdinov, R. (eds.) ICML 2019 (2019)"},{"key":"30_CR2","unstructured":"Arjovsky, M., Bottou, L., Gulrajani, I., Lopez-Paz, D.: Invariant risk minimization. arXiv preprint arXiv:1907.02893 (2019)"},{"key":"30_CR3","unstructured":"Chen, P., Liao, B.B., Chen, G., Zhang, S.: Understanding and utilizing deep neural networks trained with noisy labels. In: ICML (2019)"},{"key":"30_CR4","doi-asserted-by":"crossref","unstructured":"Chen, X., Gupta, A.: Webly supervised learning of convolutional networks. In: ICCV (2015)","DOI":"10.1109\/ICCV.2015.168"},{"key":"30_CR5","doi-asserted-by":"crossref","unstructured":"Cui, Y., Jia, M., Lin, T., Song, Y., Belongie, S.J.: Class-balanced loss based on effective number of samples. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00949"},{"key":"30_CR6","unstructured":"Foret, P., Kleiner, A., Mobahi, H., Neyshabur, B.: Sharpness-aware minimization for efficiently improving generalization. In: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, 3\u20137 May 2021. OpenReview.net (2021). https:\/\/openreview.net\/forum?id=6Tm1mposlrM"},{"issue":"5","key":"30_CR7","first-page":"845","volume":"25","author":"B Fr\u00e9nay","year":"2013","unstructured":"Fr\u00e9nay, B., Verleysen, M.: Classification in the presence of label noise: a survey. IEEE TNNLS 25(5), 845\u2013869 (2013)","journal-title":"IEEE TNNLS"},{"key":"30_CR8","doi-asserted-by":"crossref","unstructured":"Huang, Y., Bai, B., Zhao, S., Bai, K., Wang, F.: Uncertainty-aware learning against label noise on imbalanced datasets. In: Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, Thirty-Fourth Conference on Innovative Applications of Artificial Intelligence, IAAI 2022, The Twelveth Symposium on Educational Advances in Artificial Intelligence, EAAI 2022 Virtual Event, 22 February\u20131 March 2022, pp. 6960\u20136969. AAAI Press (2022). https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/20654","DOI":"10.1609\/aaai.v36i6.20654"},{"key":"30_CR9","unstructured":"Kang, B., et al.: Decoupling representation and classifier for long-tailed recognition. arXiv preprint arXiv:1910.09217 (2019)"},{"key":"30_CR10","unstructured":"Kang, B., et al.: Decoupling representation and classifier for long-tailed recognition. In: ICLR (2020)"},{"key":"30_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101759","volume":"65","author":"D Karimi","year":"2020","unstructured":"Karimi, D., Dou, H., Warfield, S.K., Gholipour, A.: Deep learning with noisy labels: exploring techniques and remedies in medical image analysis. Med. Image Anal. 65, 101759 (2020)","journal-title":"Med. Image Anal."},{"key":"30_CR12","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1007\/978-3-031-16437-8_21","volume-title":"MICCAI 2022","author":"J Li","year":"2022","unstructured":"Li, J., et al.: Flat-aware cross-stage distilled framework for imbalanced medical image classification. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13433, pp. 217\u2013226. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16437-8_21"},{"key":"30_CR13","unstructured":"Li, J., Socher, R., Hoi, S.C.H.: Dividemix: learning with noisy labels as semi-supervised learning. In: ICLR 2020 (2020)"},{"key":"30_CR14","doi-asserted-by":"crossref","unstructured":"Lin, T., Goyal, P., Girshick, R.B., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"30_CR15","doi-asserted-by":"crossref","unstructured":"Liu, J., Sun, Y., Han, C., Dou, Z., Li, W.: Deep representation learning on long-tailed data: a learnable embedding augmentation perspective. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00304"},{"key":"30_CR16","unstructured":"Ma, X., Huang, H., Wang, Y., Romano, S., Erfani, S.M., Bailey, J.: Normalized loss functions for deep learning with noisy labels. In: ICML 2020 (2020)"},{"key":"30_CR17","doi-asserted-by":"crossref","unstructured":"Mahajan, D., et al.: Exploring the limits of weakly supervised pretraining. In: ECCV (2018)","DOI":"10.1007\/978-3-030-01216-8_12"},{"key":"30_CR18","doi-asserted-by":"crossref","unstructured":"Song, H., Kim, M., Park, D., Shin, Y., Lee, J.G.: Learning from noisy labels with deep neural networks: a survey. IEEE TNNLS (2022)","DOI":"10.1109\/TNNLS.2022.3152527"},{"key":"30_CR19","doi-asserted-by":"crossref","unstructured":"Tan, C., Xia, J., Wu, L., Li, S.Z.: Co-learning: learning from noisy labels with self-supervision. In: Shen, H.T., et al. (eds.) ACM 2021 (2021)","DOI":"10.1145\/3474085.3475622"},{"key":"30_CR20","doi-asserted-by":"crossref","unstructured":"Tan, J., Lu, X., Zhang, G., Yin, C., Li, Q.: Equalization loss V2: a new gradient balance approach for long-tailed object detection. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00173"},{"key":"30_CR21","doi-asserted-by":"crossref","unstructured":"Tan, J., et al.: Equalization loss for long-tailed object recognition. In: CVPR 2020 (2020)","DOI":"10.1109\/CVPR42600.2020.01168"},{"issue":"1","key":"30_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2018.161","volume":"5","author":"P Tschandl","year":"2018","unstructured":"Tschandl, P., Rosendahl, C., Kittler, H.: The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci. Data 5(1), 1\u20139 (2018)","journal-title":"Sci. Data"},{"key":"30_CR23","doi-asserted-by":"crossref","unstructured":"Xue, C., Dou, Q., Shi, X., Chen, H., Heng, P.A.: Robust learning at noisy labeled medical images: applied to skin lesion classification. In: ISBI 2019 (2019)","DOI":"10.1109\/ISBI.2019.8759203"},{"issue":"6","key":"30_CR24","first-page":"1371","volume":"41","author":"C Xue","year":"2022","unstructured":"Xue, C., Yu, L., Chen, P., Dou, Q., Heng, P.A.: Robust medical image classification from noisy labeled data with global and local representation guided co-training. IEEE TMI 41(6), 1371\u20131382 (2022)","journal-title":"IEEE TMI"},{"key":"30_CR25","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"739","DOI":"10.1007\/978-3-031-19809-0_42","volume-title":"ECCV 2022","author":"X Yi","year":"2022","unstructured":"Yi, X., Tang, K., Hua, X.S., Lim, J.H., Zhang, H.: Identifying hard noise in long-tailed sample distribution. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13686, pp. 739\u2013756. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19809-0_42"},{"key":"30_CR26","unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017)"},{"key":"30_CR27","unstructured":"Zhang, Z., Sabuncu, M.R.: Generalized cross entropy loss for training deep neural networks with noisy labels. In: Bengio, S., Wallach, H.M., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) NIPS 2018 (2018)"},{"key":"30_CR28","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Cui, J., Liu, S., Jia, J.: Improving calibration for long-tailed recognition. In: CVPR 2021 (2021)","DOI":"10.1109\/CVPR46437.2021.01622"},{"issue":"4","key":"30_CR29","first-page":"881","volume":"41","author":"C Zhu","year":"2021","unstructured":"Zhu, C., Chen, W., Peng, T., Wang, Y., Jin, M.: Hard sample aware noise robust learning for histopathology image classification. IEEE TMI 41(4), 881\u2013894 (2021)","journal-title":"IEEE TMI"}],"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\/978-3-031-43987-2_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T15:30:51Z","timestamp":1710171051000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43987-2_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031439865","9783031439872"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43987-2_30","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"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)"}}]}}