{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T04:42:06Z","timestamp":1758343326143,"version":"3.44.0"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032051684","type":"print"},{"value":"9783032051691","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-05169-1_60","type":"book-chapter","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T21:50:38Z","timestamp":1758318638000},"page":"624-634","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Uncertainty-Aware Multi-expert Knowledge Distillation for\u00a0Imbalanced Disease Grading"],"prefix":"10.1007","author":[{"given":"Shuo","family":"Tong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shangde","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ke","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zihang","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongxia","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haochao","family":"Ying","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,20]]},"reference":[{"issue":"1","key":"60_CR1","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1038\/s41591-021-01620-2","volume":"28","author":"W Hangzhou","year":"2022","unstructured":"Hangzhou, W., et al.: Artificial intelligence for diagnosis and gleason grading of prostate cancer: the panda challenge. Nat. Med. 28(1), 154\u2013163 (2022)","journal-title":"Nat. Med."},{"key":"60_CR2","doi-asserted-by":"crossref","unstructured":"Chen, K., Liu, S., Zhu, T., Qiao, J., Su, Y., et\u00a0al.: Improving expressivity of gnns with subgraph-specific factor embedded normalization. In: ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 237\u2013249 (2023)","DOI":"10.1145\/3580305.3599388"},{"key":"60_CR3","doi-asserted-by":"crossref","unstructured":"Cheng, Y., et al.: Robust image ordinal regression with controllable image generation. arXiv preprint arXiv:2305.04213 (2023)","DOI":"10.24963\/ijcai.2023\/70"},{"issue":"1","key":"60_CR4","doi-asserted-by":"publisher","first-page":"4596","DOI":"10.1038\/s41467-024-48666-7","volume":"15","author":"A Claudio Quiros","year":"2024","unstructured":"Claudio Quiros, A., et al.: Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unannotated pathology slides. Nat. Commun. 15(1), 4596 (2024)","journal-title":"Nat. Commun."},{"issue":"1","key":"60_CR5","doi-asserted-by":"publisher","first-page":"3242","DOI":"10.1038\/s41467-021-23458-5","volume":"12","author":"L Dai","year":"2021","unstructured":"Dai, L., et al.: A deep learning system for detecting diabetic retinopathy across the disease spectrum. Nat. Commun. 12(1), 3242 (2021)","journal-title":"Nat. Commun."},{"key":"60_CR6","doi-asserted-by":"crossref","unstructured":"Gao, S., et al.: Collaborative knowledge amalgamation: preserving discriminability and transferability in unsupervised learning. Inf. Sci. 669, 120564 (2024)","DOI":"10.1016\/j.ins.2024.120564"},{"key":"60_CR7","doi-asserted-by":"crossref","unstructured":"Gao, S., Fu, Y., Liu, K., Han, Y.: Contrastive knowledge amalgamation for unsupervised image classification. In: International Conference on Artificial Neural Networks, pp. 192\u2013204. Springer (2023)","DOI":"10.1007\/978-3-031-44210-0_16"},{"key":"60_CR8","doi-asserted-by":"crossref","unstructured":"Gao, S., Fu, Y., Liu, K., Xu, H., Wu, J.: Ka 2 er: knowledge adaptive amalgamation of experts for medical images segmentation. In: MICCAI Challenge on Comprehensive Analysis and Computing of Real-World Medical Images, pp. 202\u2013214. Springer (2024)","DOI":"10.1007\/978-3-031-87009-5_20"},{"key":"60_CR9","doi-asserted-by":"crossref","unstructured":"Gao, S., Zhou, H., Gao, Y., Zhuang, X.: Bayeseg: Bayesian modeling for medical image segmentation with interpretable generalizability. Med. Image Anal. 89, 102889 (2023)","DOI":"10.1016\/j.media.2023.102889"},{"issue":"2","key":"60_CR10","doi-asserted-by":"publisher","first-page":"1405","DOI":"10.1109\/TPAMI.2022.3163307","volume":"45","author":"S Gao","year":"2022","unstructured":"Gao, S., Zhuang, X.: Bayesian image super-resolution with deep modeling of image statistics. IEEE Trans. Pattern Anal. Mach. Intell. 45(2), 1405\u20131423 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"60_CR11","unstructured":"Hao, Z., et al.: One-for-all: Bridge the gap between heterogeneous architectures in knowledge distillation. In: Advances in Neural Information Processing Systems, vol. 36 (2024)"},{"key":"60_CR12","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"60_CR13","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)"},{"key":"60_CR14","doi-asserted-by":"crossref","unstructured":"Huang, Z., Wang, Z., Zhao, T., Ding, X., Yang, X.: Toward high-quality pseudo masks from noisy or weak annotations for robust medical image segmentation. Neural Netw. 181, 106850 (2025)","DOI":"10.1016\/j.neunet.2024.106850"},{"key":"60_CR15","doi-asserted-by":"crossref","unstructured":"Huang, Z., Yang, Y., Zhao, T., Yang, X.: A noise robust framework via uncertainty guidance for medical image segmentation with noisy label. In: 2024 IEEE International Conference on Multimedia and Expo (ICME), pp.\u00a01\u20136. IEEE (2024)","DOI":"10.1109\/ICME57554.2024.10687399"},{"key":"60_CR16","unstructured":"Karthik, Maggie, Dane, S.: Aptos 2019 blindness detection. https:\/\/kaggle.com\/competitions\/aptos2019-blindness-detection (2019), kaggle"},{"key":"60_CR17","unstructured":"Li, L., Li, X.C., Ye, H.J., Zhan, D.C.: Enhancing class-imbalanced learning with pre-trained guidance through class-conditional knowledge distillation. In: Forty-first International Conference on Machine Learning (2024)"},{"issue":"6","key":"60_CR18","doi-asserted-by":"publisher","first-page":"1789","DOI":"10.1109\/TPDS.2023.3264473","volume":"34","author":"NJ Mohan","year":"2023","unstructured":"Mohan, N.J., Murugan, R., Goel, T., Roy, P.: Drfl: federated learning in diabetic retinopathy grading using fundus images. IEEE Trans. Parallel Distrib. Syst. 34(6), 1789\u20131801 (2023)","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"60_CR19","doi-asserted-by":"crossref","unstructured":"Park, W., Kim, D., Lu, Y., Cho, M.: Relational knowledge distillation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3967\u20133976 (2019)","DOI":"10.1109\/CVPR.2019.00409"},{"key":"60_CR20","unstructured":"Porwal, P., et al.: Idrid: diabetic retinopathy-segmentation and grading challenge. Med. Image Anal. 59, 101561 (2020)"},{"key":"60_CR21","unstructured":"Romero, A., Ballas, N., Kahou, S.E., Chassang, A., Gatta, C., Bengio, Y.: Fitnets: hints for thin deep nets. arXiv preprint arXiv:1412.6550 (2014)"},{"key":"60_CR22","doi-asserted-by":"crossref","unstructured":"Silva-Rodr\u00edguez, J., Colomer, A., Sales, M.A., Molina, R., Naranjo, V.: Going deeper through the gleason scoring scale: an automatic end-to-end system for histology prostate grading and cribriform pattern detection. Comput. Methods Programs Biomed. 195, 105637 (2020)","DOI":"10.1016\/j.cmpb.2020.105637"},{"key":"60_CR23","doi-asserted-by":"crossref","unstructured":"Wang, J., Cheng, Y., Chen, J., Chen, T., Chen, D., Wu, J.: Ord2seq: regarding ordinal regression as label sequence prediction. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5865\u20135875 (2023)","DOI":"10.1109\/ICCV51070.2023.00539"},{"issue":"3","key":"60_CR24","doi-asserted-by":"publisher","first-page":"1089","DOI":"10.1109\/TMI.2023.3327274","volume":"43","author":"J Wang","year":"2023","unstructured":"Wang, J., et al.: A transformer-based knowledge distillation network for cortical cataract grading. IEEE Trans. Med. Imaging 43(3), 1089\u20131101 (2023)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"60_CR25","doi-asserted-by":"crossref","unstructured":"Wei, S., Luo, C., Luo, Y.: Scaled decoupled distillation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15975\u201315983 (2024)","DOI":"10.1109\/CVPR52733.2024.01512"},{"key":"60_CR26","doi-asserted-by":"crossref","unstructured":"Xie, X., Niu, J., Liu, X., Chen, Z., Tang, S., Yu, S.: A survey on incorporating domain knowledge into deep learning for medical image analysis. Med. Image Anal. 69, 101985 (2021)","DOI":"10.1016\/j.media.2021.101985"},{"key":"60_CR27","doi-asserted-by":"crossref","unstructured":"Zhao, B., Cui, Q., Song, R., Qiu, Y., Liang, J.: Decoupled knowledge distillation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11953\u201311962 (2022)","DOI":"10.1109\/CVPR52688.2022.01165"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-05169-1_60","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T21:50:45Z","timestamp":1758318645000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05169-1_60"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,20]]},"ISBN":["9783032051684","9783032051691"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05169-1_60","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,20]]},"assertion":[{"value":"20 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"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":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}