{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T15:14:33Z","timestamp":1751555673185,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031164361"},{"type":"electronic","value":"9783031164378"}],"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.springer.com\/tdm"},{"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.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-16437-8_21","type":"book-chapter","created":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T18:13:04Z","timestamp":1663265584000},"page":"217-226","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Flat-Aware Cross-Stage Distilled Framework for\u00a0Imbalanced Medical Image Classification"],"prefix":"10.1007","author":[{"given":"Jinpeng","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangyong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hangyu","family":"Mao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Danruo","family":"Deng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dong","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianye","family":"Hao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Dou","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":[[2022,9,16]]},"reference":[{"key":"21_CR1","unstructured":"Diabetic retinopathy detection. In: Kaggle. https:\/\/www.kaggle.com\/c\/diabetic-retinopathy-detection"},{"key":"21_CR2","unstructured":"Endotect challenge, ICPR 2020. https:\/\/endotect.com\/"},{"key":"21_CR3","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/j.neunet.2018.07.011","volume":"106","author":"M Buda","year":"2018","unstructured":"Buda, M., Maki, A., Mazurowski, M.A.: A systematic study of the class imbalance problem in convolutional neural networks. Neural Netw. 106, 249\u2013259 (2018)","journal-title":"Neural Netw."},{"key":"21_CR4","unstructured":"Cao, K., Wei, C., Gaidon, A., Ar\u00e9chiga, N., Ma, T.: Learning imbalanced datasets with label-distribution-aware margin loss. In: Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems, NeurIPS, pp. 1565\u20131576 (2019)"},{"key":"21_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: IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 9268\u20139277 (2019)","DOI":"10.1109\/CVPR.2019.00949"},{"key":"21_CR6","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR, pp. 248\u2013255 (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"21_CR7","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)"},{"key":"21_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1007\/978-3-030-87240-3_31","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"A Galdran","year":"2021","unstructured":"Galdran, A., Carneiro, G., Gonz\u00e1lez Ballester, M.A.: Balanced-MixUp for highly imbalanced medical image classification. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12905, pp. 323\u2013333. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87240-3_31"},{"key":"21_CR9","doi-asserted-by":"crossref","unstructured":"He, Y., Wu, J., Wei, X.: Distilling virtual examples for long-tailed recognition. In: IEEE International Conference on Computer Vision, ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00030"},{"key":"21_CR10","unstructured":"Hinton, G., Vinyals, O., Dean, J., et al.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)"},{"key":"21_CR11","unstructured":"Kang, B. et al.: Decoupling representation and classifier for long-tailed recognition. In: 8th International Conference on Learning Representations, ICLR (2020)"},{"key":"21_CR12","unstructured":"Li, H., Xu, Z., Taylor, G., Studer, C., Goldstein, T.: Visualizing the loss landscape of neural nets. In: Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems, NeurIPS, pp. 6391\u20136401 (2018)"},{"key":"21_CR13","doi-asserted-by":"crossref","unstructured":"Lin, T., Goyal, P., Girshick, R.B., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: IEEE International Conference on Computer Vision, ICCV, pp. 2999\u20133007 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"21_CR14","doi-asserted-by":"crossref","unstructured":"Mahajan, D. et al.: Exploring the limits of weakly supervised pretraining. In: Computer Vision - ECCV - 15th European Conference, pp. 185\u2013201 (2018)","DOI":"10.1007\/978-3-030-01216-8_12"},{"key":"21_CR15","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.neucom.2021.10.021","volume":"469","author":"Z Mai","year":"2022","unstructured":"Mai, Z., Li, R., Jeong, J., Quispe, D., Kim, H., Sanner, S.: Online continual learning in image classification: an empirical survey. Neurocomputing 469, 28\u201351 (2022)","journal-title":"Neurocomputing"},{"key":"21_CR16","doi-asserted-by":"crossref","unstructured":"Oksuz, K., Cam, B.C., Akbas, E., Kalkan, S.: Rank & sort loss for object detection and instance segmentation. In: IEEE International Conference on Computer Vision, ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00300"},{"key":"21_CR17","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobileNetV 2: inverted residuals and linear bottlenecks. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 4510\u20134520 (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"21_CR18","doi-asserted-by":"crossref","unstructured":"Sarafianos, N., Xu, X., Kakadiaris, I.A.: Deep imbalanced attribute classification using visual attention aggregation. In: Computer Vision - ECCV - 15th European Conference, pp. 708\u2013725 (2018)","DOI":"10.1007\/978-3-030-01252-6_42"},{"issue":"1","key":"21_CR19","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","volume":"19","author":"D Shen","year":"2017","unstructured":"Shen, D., Wu, G., Suk, H.I.: Deep learning in medical image analysis. Ann. Rev. Biomed. Eng. 19(1), 221\u2013248 (2017)","journal-title":"Ann. Rev. Biomed. Eng."},{"key":"21_CR20","unstructured":"Shi, G., Chen, J., Zhang, W., Zhan, L., Wu, X.: Overcoming catastrophic forgetting in incremental few-shot learning by finding flat minima. In: Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing System, NeurIPS, pp. 6747\u20136761 (2021)"},{"key":"21_CR21","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: IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 1685\u20131694 (2021)","DOI":"10.1109\/CVPR46437.2021.00173"},{"key":"21_CR22","doi-asserted-by":"crossref","unstructured":"Tan, J. et al.: Equalization loss for long-tailed object recognition. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR, pp. 11659\u201311668 (2020)","DOI":"10.1109\/CVPR42600.2020.01168"},{"key":"21_CR23","doi-asserted-by":"crossref","unstructured":"Wang, P., Han, K., Wei, X., Zhang, L., Wang, L.: Contrastive learning based hybrid networks for long-tailed image classification. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 943\u2013952 (2021)","DOI":"10.1109\/CVPR46437.2021.00100"},{"key":"21_CR24","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Wei, X., Zhou, B., Wu, J.: Bag of tricks for long-tailed visual recognition with deep convolutional neural networks. In: Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI, pp. 3447\u20133455 (2021)","DOI":"10.1609\/aaai.v35i4.16458"},{"key":"21_CR25","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Cui, J., Liu, S., Jia, J.: Improving calibration for long-tailed recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 16489\u201316498 (2021)","DOI":"10.1109\/CVPR46437.2021.01622"},{"key":"21_CR26","doi-asserted-by":"crossref","unstructured":"Zhou, B., Cui, Q., Wei, X., Chen, Z.: BBN: bilateral-branch network with cumulative learning for long-tailed visual recognition. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR, pp. 9716\u20139725 (2020)","DOI":"10.1109\/CVPR42600.2020.00974"},{"key":"21_CR27","doi-asserted-by":"publisher","first-page":"820","DOI":"10.1109\/JPROC.2021.3054390","volume":"109","author":"SK Zhou","year":"2021","unstructured":"Zhou, S.K., et al.: A review of deep learning in medical imaging: imaging traits, technology trends, case studies with progress highlights, and future promises. Proc. IEEE 109, 820\u2013838 (2021)","journal-title":"Proc. IEEE"}],"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\/978-3-031-16437-8_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T14:04:54Z","timestamp":1710252294000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16437-8_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164361","9783031164378"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16437-8_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"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)"}}]}}