{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T03:15:06Z","timestamp":1782962106703,"version":"3.54.5"},"publisher-location":"Cham","reference-count":45,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030871987","type":"print"},{"value":"9783030871994","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\/978-3-030-87199-4_44","type":"book-chapter","created":{"date-parts":[[2021,9,23]],"date-time":"2021-09-23T06:19:41Z","timestamp":1632377981000},"page":"466-476","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Fighting Class Imbalance with\u00a0Contrastive Learning"],"prefix":"10.1007","author":[{"given":"Yassine","family":"Marrakchi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Osama","family":"Makansi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Brox","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,9,21]]},"reference":[{"key":"44_CR1","unstructured":"Aptos 2019 blindness detection (2019). https:\/\/www.kaggle.com\/c\/aptos2019-blindness-detection\/data"},{"key":"44_CR2","unstructured":"Aleksey Nozdryn-Plotnicki, J.Y., Yolland, W.: Ensembling convolutional neural networks for skin cancer classification. ArXiv (2018)"},{"key":"44_CR3","unstructured":"Cao, K., Wei, C., Gaidon, A., Ar\u00e9chiga, N., Ma, T.: Learning imbalanced datasets with label-distribution-aware margin loss. In: NeurIPS (2019)"},{"key":"44_CR4","doi-asserted-by":"crossref","unstructured":"Chan, H.P., Samala, R.K., Hadjiiski, L.M., Zhou, C.: Deep learning in medical image analysis (2020)","DOI":"10.1007\/978-3-030-33128-3_1"},{"key":"44_CR5","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. JAIR 16, 321\u2013357 (2002)","journal-title":"JAIR"},{"key":"44_CR6","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.E.: A simple framework for contrastive learning of visual representations. In: ICML (2020)"},{"key":"44_CR7","unstructured":"Codella, N.C.F., et al.: Skin lesion analysis toward melanoma detection 2018: a challenge hosted by the international skin imaging collaboration (ISIC). CoRR abs\/1902.03368 (2019)"},{"key":"44_CR8","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":"44_CR9","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1016\/j.compmedimag.2007.02.002","volume":"31","author":"K Doi","year":"2007","unstructured":"Doi, K.: Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput. Med. Imaging Graph. 31, 198\u2013211 (2007)","journal-title":"Comput. Med. Imaging Graph."},{"key":"44_CR10","doi-asserted-by":"publisher","first-page":"1367","DOI":"10.1109\/TPAMI.2018.2832629","volume":"41","author":"Q Dong","year":"2019","unstructured":"Dong, Q., Gong, S., Zhu, X.: Imbalanced deep learning by minority class incremental rectification. IEEE TPAMI 41, 1367\u20131381 (2019)","journal-title":"IEEE TPAMI"},{"key":"44_CR11","doi-asserted-by":"crossref","unstructured":"Dosovitskiy, A., Fischer, P., Springenberg, J.T., Riedmiller, M.A., Brox, T.: Discriminative unsupervised feature learning with exemplar convolutional neural networks. IEEE TPAMI, 1734\u20131747 (2016)","DOI":"10.1109\/TPAMI.2015.2496141"},{"key":"44_CR12","unstructured":"Drummond, C., Holte, R.: C4.5, class imbalance, and cost sensitivity: why under-sampling beats oversampling. In: ICML Workshop (2003)"},{"key":"44_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"699","DOI":"10.1007\/978-3-030-59710-8_68","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"G Fotedar","year":"2020","unstructured":"Fotedar, G., Tajbakhsh, N., Ananth, S., Ding, X.: Extreme consistency: overcoming annotation scarcity and domain shifts. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 699\u2013709. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59710-8_68"},{"key":"44_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1007\/978-3-030-59713-9_57","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"L Gong","year":"2020","unstructured":"Gong, L., Ma, K., Zheng, Y.: Distractor-aware neuron intrinsic learning for generic 2D medical image classifications. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12262, pp. 591\u2013601. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59713-9_57"},{"key":"44_CR15","doi-asserted-by":"crossref","unstructured":"Guanjin Wang, K.W.W., Lu, J.: AUC-based extreme learning machines for supervised and semi-supervised imbalanced classification. IEEE Trans. Syst. Man Cybern.: Syst., 1\u201312 (2020)","DOI":"10.1109\/TSMC.2020.2982226"},{"key":"44_CR16","unstructured":"Gutmann, M., Hyv\u00e4rinen, A.: Noise-contrastive estimation: a new estimation principle for unnormalized statistical models. In: AISTATS (2010)"},{"key":"44_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"604","DOI":"10.1007\/978-3-030-59710-8_59","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"PK Gyawali","year":"2020","unstructured":"Gyawali, P.K., Ghimire, S., Bajracharya, P., Li, Z., Wang, L.: Semi-supervised medical image classification with global latent mixing. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 604\u2013613. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59710-8_59"},{"key":"44_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"878","DOI":"10.1007\/11538059_91","volume-title":"Advances in Intelligent Computing","author":"H Han","year":"2005","unstructured":"Han, H., Wang, W.-Y., Mao, B.-H.: Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In: Huang, D.-S., Zhang, X.-P., Huang, G.-B. (eds.) ICIC 2005. LNCS, vol. 3644, pp. 878\u2013887. Springer, Heidelberg (2005). https:\/\/doi.org\/10.1007\/11538059_91"},{"key":"44_CR19","doi-asserted-by":"crossref","unstructured":"Hayat, M., Khan, S., Zamir, S.W., Shen, J., Shao, L.: Gaussian affinity for max-margin class imbalanced learning. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00657"},{"key":"44_CR20","first-page":"1263","volume":"21","author":"H He","year":"2009","unstructured":"He, H., Garcia, E.A.: Learning from imbalanced data. IEEE TKDE 21, 1263\u20131284 (2009)","journal-title":"IEEE TKDE"},{"key":"44_CR21","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"44_CR22","doi-asserted-by":"crossref","unstructured":"Huang, C., Li, Y., Loy, C.C., Tang, X.: Learning deep representation for imbalanced classification. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.580"},{"key":"44_CR23","unstructured":"Kang, B., et al.: Decoupling representation and classifier for long-tailed recognition. In: ICLR (2020)"},{"key":"44_CR24","doi-asserted-by":"crossref","unstructured":"Khan, S.H., Hayat, M., Zamir, S.W., Shen, J., Shao, L.: Striking the right balance with uncertainty. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00019"},{"key":"44_CR25","unstructured":"Khosla, P., et al.: Supervised contrastive learning. In: NeurIPS (2020)"},{"key":"44_CR26","doi-asserted-by":"crossref","unstructured":"Kim, J., Jeong, J., Shin, J.: M2m: imbalanced classification via major-to-minor translation. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01391"},{"key":"44_CR27","doi-asserted-by":"crossref","unstructured":"Li, X., Hu, X., Yu, L., Zhu, L., Fu, C.W., Heng, P.: CANet: cross-disease attention network for joint diabetic retinopathy and diabetic macular edema grading. IEEE Trans. Med. Imaging, 1483\u20131493 (2020)","DOI":"10.1109\/TMI.2019.2951844"},{"key":"44_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1007\/978-3-030-59710-8_35","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"X Li","year":"2020","unstructured":"Li, X., Yu, L., Jin, Y., Fu, C.-W., Xing, L., Heng, P.-A.: Difficulty-aware meta-learning for rare disease diagnosis. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 357\u2013366. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59710-8_35"},{"key":"44_CR29","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1007\/978-3-030-59710-8_17","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"Z Li","year":"2020","unstructured":"Li, Z., Zhong, C., Wang, R., Zheng, W.-S.: Continual learning of new diseases with dual distillation and ensemble strategy. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 169\u2013178. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59710-8_17"},{"key":"44_CR30","unstructured":"Liao, H., Luo, J.: A deep multi-task learning approach to skin lesion classification. In: AAAI workshop (2017)"},{"key":"44_CR31","doi-asserted-by":"crossref","unstructured":"Lin, T., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"44_CR32","unstructured":"Loshchilov, I., Hutter, F.: SGDR: stochastic gradient descent with warm restarts. In: ICLR (2017)"},{"key":"44_CR33","doi-asserted-by":"crossref","unstructured":"Mullick, S.S., Datta, S., Das, S.: Generative adversarial minority oversampling. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00178"},{"key":"44_CR34","unstructured":"van den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. CoRR abs\/1807.03748 (2018)"},{"key":"44_CR35","unstructured":"Peilin Zhao, Steven C. H. Hoi, R.J., Yang, T.: Online AUC maximization. In: ICML (2011)"},{"key":"44_CR36","doi-asserted-by":"crossref","unstructured":"Peng, J., Bu, X., Sun, M., Zhang, Z., Tan, T., Yan, J.: Large-scale object detection in the wild from imbalanced multi-labels. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00973"},{"key":"44_CR37","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115, 211\u2013252 (2015)","journal-title":"Int. J. Comput. Vis."},{"key":"44_CR38","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1007\/978-3-319-46478-7_29","volume-title":"Computer Vision \u2013 ECCV 2016","author":"L Shen","year":"2016","unstructured":"Shen, L., Lin, Z., Huang, Q.: Relay backpropagation for effective learning of deep convolutional neural networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 467\u2013482. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46478-7_29"},{"key":"44_CR39","doi-asserted-by":"crossref","unstructured":"Shrivastava, A., Gupta, A., Girshick, R.B.: Training region-based object detectors with online hard example mining. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.89"},{"key":"44_CR40","doi-asserted-by":"crossref","unstructured":"Shrivastava, A., Gupta, A., Girshick, R: Training region-based object detectors with online hard example mining. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.89"},{"key":"44_CR41","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1007\/s13353-015-0281-x","volume":"56","author":"A Skorczyk-Werner","year":"2015","unstructured":"Skorczyk-Werner, A., et al.: Fundus albipunctatus: review of the literature and report of a novel RDH5 gene mutation affecting the invariant tyrosine (p. Tyr175Phe). J. Appl. Genet. 56, 317\u2013327 (2015)","journal-title":"J. Appl. Genet."},{"key":"44_CR42","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1007\/978-3-030-59710-8_11","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"D Wei","year":"2020","unstructured":"Wei, D., Cao, S., Ma, K., Zheng, Y.: Learning and exploiting interclass visual correlations for medical image classification. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 106\u2013115. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59710-8_11"},{"key":"44_CR43","doi-asserted-by":"crossref","unstructured":"Zhang, X., Fang, Z., Wen, Y., Li, Z., Qiao, Y.: Range loss for deep face recognition with long-tailed training data. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.578"},{"key":"44_CR44","unstructured":"Zhuang, J.X., et al.: Skin lesion analysis towards melanoma detection using deep neural network ensemble (2018)"},{"key":"44_CR45","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1007\/978-3-030-59710-8_13","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"J Zhuang","year":"2020","unstructured":"Zhuang, J., Cai, J., Wang, R., Zhang, J., Zheng, W.-S.: Deep kNN for medical image classification. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 127\u2013136. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59710-8_13"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87199-4_44","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,23]],"date-time":"2021-09-23T06:34:42Z","timestamp":1632378882000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87199-4_44"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030871987","9783030871994"],"references-count":45,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87199-4_44","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":"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":"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":"27 September 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":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miccai2021.org\/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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1622","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":"531","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":"33% - 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":"4","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)"}},{"value":"The conference was held virtually.","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)"}}]}}