{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T17:52:44Z","timestamp":1786038764996,"version":"3.56.0"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031708152","type":"print"},{"value":"9783031708169","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-70816-9_22","type":"book-chapter","created":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T16:02:28Z","timestamp":1725552148000},"page":"279-291","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Intelligent Handling of\u00a0Noise in\u00a0Federated Learning with\u00a0Co-training for\u00a0Enhanced Diagnostic Precision"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-0073-2045","authenticated-orcid":false,"given":"Farah Farid","family":"Babar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1994-6907","authenticated-orcid":false,"given":"Faisal","family":"Jamil","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-2899-2397","authenticated-orcid":false,"given":"Faiza Fareed","family":"Babar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,8,28]]},"reference":[{"key":"22_CR1","doi-asserted-by":"crossref","unstructured":"Jamil, F., Hameed, I.A.: Toward intelligent open-ended questions evaluation based on predictive optimization. Expert Syst. Appl. 120640 (2023)","DOI":"10.1016\/j.eswa.2023.120640"},{"issue":"2","key":"22_CR2","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1038\/s41591-020-01174-9","volume":"27","author":"W Lotter","year":"2021","unstructured":"Lotter, W., et al.: Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach. Nat. Med. 27(2), 244\u2013249 (2021)","journal-title":"Nat. Med."},{"issue":"8","key":"22_CR3","first-page":"20","volume":"10","author":"FF Babar","year":"2023","unstructured":"Babar, F.F., Lukui, S., Babar, F.F., Muhammad, F.: Enhanced weather forecasting using the meteronet model: a comprehensive ensemble approach. Int. J. Adv. Multidisc. Res. 10(8), 20\u201338 (2023)","journal-title":"Int. J. Adv. Multidisc. Res."},{"key":"22_CR4","doi-asserted-by":"publisher","first-page":"1256351","DOI":"10.3389\/fnins.2023.1256351","volume":"17","author":"Y Wang","year":"2023","unstructured":"Wang, Y., Liu, L., Wang, C.: Trends in using deep learning algorithms in biomedical prediction systems. Front. Neurosci. 17, 1256351 (2023)","journal-title":"Front. Neurosci."},{"issue":"1","key":"22_CR5","doi-asserted-by":"publisher","first-page":"1953","DOI":"10.1038\/s41598-022-05539-7","volume":"12","author":"M Adnan","year":"2022","unstructured":"Adnan, M., Kalra, S., Cresswell, J.C., Taylor, G.W., Tizhoosh, H.R.: Federated learning and differential privacy for medical image analysis. Sci. Rep. 12(1), 1953 (2022)","journal-title":"Sci. Rep."},{"issue":"3","key":"22_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3501296","volume":"55","author":"DC Nguyen","year":"2022","unstructured":"Nguyen, D.C., et al.: Federated learning for smart healthcare: a survey. ACM Comput. Surv. (CSUR) 55(3), 1\u201337 (2022)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"22_CR7","doi-asserted-by":"publisher","first-page":"108327","DOI":"10.1016\/j.cie.2022.108327","volume":"170","author":"F Jamil","year":"2022","unstructured":"Jamil, F., Ahmad, S., Whangbo, T.K., Muthanna, A., Kim, D.-H.: Improving blockchain performance in clinical trials using intelligent optimal transaction traffic control mechanism in smart healthcare applications. Comput. Industr. Eng. 170, 108327 (2022)","journal-title":"Comput. Industr. Eng."},{"key":"22_CR8","doi-asserted-by":"crossref","unstructured":"Jamil, F., Qayyum, F., Alhelaly, S., Javed, F., Muthanna, A.: Intelligent microservice based on blockchain for healthcare applications. Comput. Mater. Continua 69(2) (2021)","DOI":"10.32604\/cmc.2021.018809"},{"issue":"5","key":"22_CR9","doi-asserted-by":"publisher","first-page":"8991","DOI":"10.3233\/JIFS-201352","volume":"40","author":"F Jamil","year":"2021","unstructured":"Jamil, F., Kim, D.H.: Enhanced Kalman filter algorithm using fuzzy inference for improving position estimation in indoor navigation. J. Intell. Fuzzy Syst. 40(5), 8991\u20139005 (2021)","journal-title":"J. Intell. Fuzzy Syst."},{"issue":"4","key":"22_CR10","doi-asserted-by":"publisher","first-page":"881","DOI":"10.1109\/TMI.2021.3125459","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 Trans. Med. Imaging 41(4), 881\u2013894 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"12","key":"22_CR11","doi-asserted-by":"publisher","first-page":"3580","DOI":"10.1109\/TMI.2021.3091178","volume":"40","author":"J Liu","year":"2021","unstructured":"Liu, J., Li, R., Sun, C.: Co-correcting: noise-tolerant medical image classification via mutual label correction. IEEE Trans. Med. Imaging 40(12), 3580\u20133592 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"2","key":"22_CR12","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1109\/MIS.2022.3151466","volume":"37","author":"S Yang","year":"2022","unstructured":"Yang, S., Park, H., Byun, J., Kim, C.: Robust federated learning with noisy labels. IEEE Intell. Syst. 37(2), 35\u201343 (2022)","journal-title":"IEEE Intell. Syst."},{"key":"22_CR13","doi-asserted-by":"crossref","unstructured":"Tam, K., Li, L., Han, B., Xu, C., Fu, H.: Federated noisy client learning. IEEE Trans. Neural Netw. Learn. Syst. (2023)","DOI":"10.1109\/TNNLS.2023.3336050"},{"key":"22_CR14","doi-asserted-by":"crossref","unstructured":"Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proceedings of the Eleventh Annual Conference on Computational Learning Theory, pp. 92\u2013100 (1998)","DOI":"10.1145\/279943.279962"},{"key":"22_CR15","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., y\u00a0Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273\u20131282. PMLR (2017)"},{"key":"22_CR16","unstructured":"Han, B., et al.: Co-teaching: robust training of deep neural networks with extremely noisy labels. In: Advances in Neural Information Processing Systems, vol. 31 (2018)"},{"issue":"1\u20132","key":"22_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/2200000083","volume":"14","author":"P Kairouz","year":"2021","unstructured":"Kairouz, P., et al.: Advances and open problems in federated learning. Found. Trends\u00ae Mach. Learn. 14(1\u20132), 1\u2013210 (2021)","journal-title":"Found. Trends\u00ae Mach. Learn."},{"key":"22_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1007\/978-3-030-32692-0_16","volume-title":"Machine Learning in Medical Imaging","author":"W Li","year":"2019","unstructured":"Li, W., et al.: Privacy-preserving federated brain tumour segmentation. In: Suk, H.-I., Liu, M., Yan, P., Lian, C. (eds.) MLMI 2019. LNCS, vol. 11861, pp. 133\u2013141. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32692-0_16"},{"issue":"12","key":"22_CR19","doi-asserted-by":"publisher","first-page":"3663","DOI":"10.1109\/TMI.2022.3192483","volume":"41","author":"Z Chen","year":"2022","unstructured":"Chen, Z., Yang, C., Zhu, M., Peng, Z., Yuan, Y.: Personalized retrogress-resilient federated learning toward imbalanced medical data. IEEE Trans. Med. Imaging 41(12), 3663\u20133674 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"22_CR20","doi-asserted-by":"publisher","first-page":"101759","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":"22_CR21","doi-asserted-by":"crossref","unstructured":"Patrini, G., Rozza, A., Menon, A.K., Nock, R., Qu, L.: Making deep neural networks robust to label noise: a loss correction approach. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1944\u20131952 (2017)","DOI":"10.1109\/CVPR.2017.240"},{"key":"22_CR22","unstructured":"Zhang, Z., Sabuncu, M.: Generalized cross entropy loss for training deep neural networks with noisy labels. In: Advances in Neural Information Processing Systems, vol. 31 (2018)"},{"key":"22_CR23","unstructured":"Menon, A.K., Rawat, A.S., Reddi, S.J., Kumar, S.: Can gradient clipping mitigate label noise? In: International Conference on Learning Representations (2019)"},{"issue":"6","key":"22_CR24","doi-asserted-by":"publisher","first-page":"1533","DOI":"10.1109\/TMI.2022.3141425","volume":"41","author":"J Lie","year":"2022","unstructured":"Lie, J., et al.: Improving medical images classification with label noise using dual-uncertainty estimation. IEEE Trans. Med. Imaging 41(6), 1533\u20131546 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"22_CR25","unstructured":"Goldberger, J., Ben-Reuven, E.: Training deep neural-networks using a noise adaptation layer. In: International Conference on Learning Representations (2022)"},{"key":"22_CR26","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: International Conference on Machine Learning, pp. 2304\u20132313. PMLR (2018)"},{"key":"22_CR27","unstructured":"Sukhbaatar, S., Bruna, J., Paluri, M., Bourdev, L., Fergus, R.: Training convolutional networks with noisy labels. arXiv preprint arXiv:1406.2080 (2014)"},{"issue":"6","key":"22_CR28","doi-asserted-by":"publisher","first-page":"1371","DOI":"10.1109\/TMI.2021.3140140","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 Trans. Med. Imaging 41(6), 1371\u20131382 (2022)","journal-title":"IEEE Trans. Med. Imaging"}],"container-title":["Lecture Notes in Computer Science","Computational Collective Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-70816-9_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T16:06:42Z","timestamp":1725552402000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-70816-9_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031708152","9783031708169"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-70816-9_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"28 August 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCCI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Collective Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Leipzig","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccci2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iccci.pwr.edu.pl\/2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}