{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T07:47:09Z","timestamp":1758268029906,"version":"3.44.0"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032058690","type":"print"},{"value":"9783032058706","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T00:00:00Z","timestamp":1758240000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T00:00:00Z","timestamp":1758240000000},"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\/978-3-032-05870-6_8","type":"book-chapter","created":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T11:28:38Z","timestamp":1758194918000},"page":"74-83","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Exploring the\u00a0Interplay of\u00a0Label Bias with\u00a0Subgroup Size and\u00a0Separability: A Case Study in\u00a0Mammographic Density Classification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7802-6820","authenticated-orcid":false,"given":"Emma A. M.","family":"Stanley","sequence":"first","affiliation":[]},{"given":"Raghav","family":"Mehta","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6739-1638","authenticated-orcid":false,"given":"M\u00e9lanie","family":"Roschewitz","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2556-3224","authenticated-orcid":false,"given":"Nils D.","family":"Forkert","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4897-9356","authenticated-orcid":false,"given":"Ben","family":"Glocker","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,19]]},"reference":[{"key":"8_CR1","doi-asserted-by":"crossref","unstructured":"Chen, R.J., et al.: Algorithmic fairness in artificial intelligence for medicine and healthcare. Nat. Biomed. Eng. 7(6), 719\u2013742 (2023)","DOI":"10.1038\/s41551-023-01056-8"},{"issue":"5","key":"8_CR2","doi-asserted-by":"publisher","first-page":"845","DOI":"10.1109\/TNNLS.2013.2292894","volume":"25","author":"B Frenay","year":"2014","unstructured":"Frenay, B., Verleysen, M.: Classification in the presence of label noise: a survey. IEEE Trans. Neural Netw. Learn. Syst. 25(5), 845\u2013869 (2014). May","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"6","key":"8_CR3","doi-asserted-by":"publisher","first-page":"e406","DOI":"10.1016\/S2589-7500(22)00063-2","volume":"4","author":"JW Gichoya","year":"2022","unstructured":"Gichoya, J.W., et al.: Ai recognition of patient race in medical imaging: a modelling study. Lancet Digital Health 4(6), e406\u2013e414 (2022)","journal-title":"Lancet Digital Health"},{"key":"8_CR4","doi-asserted-by":"crossref","unstructured":"Glocker, B., Jones, C., Bernhardt, M., Winzeck, S.: Algorithmic encoding of protected characteristics in chest x-ray disease detection models. eBioMedicine 89, 104467 (2023)","DOI":"10.1016\/j.ebiom.2023.104467"},{"key":"8_CR5","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (Jun 2016), iSSN: 1063-6919","DOI":"10.1109\/CVPR.2016.90"},{"key":"8_CR6","doi-asserted-by":"crossref","unstructured":"Jain, S., et al.: VisualCheXbert: addressing the discrepancy between radiology report labels and image labels. In: Proceedings of the Conference on Health, Inference, and Learning, CHIL 2021, pp. 105\u2013115. Association for Computing Machinery, New York (Apr 2021)","DOI":"10.1145\/3450439.3451862"},{"key":"8_CR7","doi-asserted-by":"crossref","unstructured":"Jeong, J.J., et al.: The emory breast imaging dataset (embed): a racially diverse, granular dataset of 3.4 million screening and diagnostic mammographic images. Radiology: Artifi. Intell. 5(1), e220047 (2023)","DOI":"10.1148\/ryai.220047"},{"key":"8_CR8","doi-asserted-by":"crossref","unstructured":"Jones, C., Castro, D.C., De\u00a0Sousa\u00a0Ribeiro, F., Oktay, O., McCradden, M., Glocker, B.: A causal perspective on dataset bias in machine learning for medical imaging. Nat. Mach. Intell., 1\u20139 (2024)","DOI":"10.1038\/s42256-024-00797-8"},{"key":"8_CR9","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1007\/978-3-031-43898-1_18","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023","author":"C Jones","year":"2023","unstructured":"Jones, C., Roschewitz, M., Glocker, B.: The role of subgroup separability in group-fair medical image classification. In: Greenspan, H., et al. (eds.) Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023, pp. 179\u2013188. Lecture Notes in Computer Science, Springer Nature Switzerland, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-43898-1_18"},{"key":"8_CR10","doi-asserted-by":"crossref","unstructured":"Markowitz, D.M.: Gender and ethnicity bias in medicine: a text analysis of 1.8 million critical care records. PNAS Nexus 1(4), pgac157 (2022)","DOI":"10.1093\/pnasnexus\/pgac157"},{"key":"8_CR11","doi-asserted-by":"crossref","unstructured":"Mollura, D.J., et al.: Artificial intelligence in low- and middle-income countries: innovating global health radiology. Radiology 297(3), 513\u2013520 (2020)","DOI":"10.1148\/radiol.2020201434"},{"key":"8_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.59275\/j.melba.2022-2d93","volume":"1","author":"B Nichyporuk","year":"2022","unstructured":"Nichyporuk, B., et al.: Rethinking generalization: the impact of annotation style on medical image segmentation. Mach Learn. Biomed. Imaging 1, 1\u201337 (2022)","journal-title":"Mach Learn. Biomed. Imaging"},{"issue":"7","key":"8_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.patter.2023.100790","volume":"4","author":"E Petersen","year":"2023","unstructured":"Petersen, E., Holm, S., Ganz, M., Feragen, A.: The path toward equal performance in medical machine learning. Patterns 4(7), 100790 (2023). Jul","journal-title":"Patterns"},{"key":"8_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103166","volume":"95","author":"J Shi","year":"2024","unstructured":"Shi, J., Zhang, K., Guo, C., Yang, Y., Xu, Y., Wu, J.: A survey of label-noise deep learning for medical image analysis. Med. Image Anal. 95, 103166 (2024). Jul","journal-title":"Med. Image Anal."},{"key":"8_CR15","unstructured":"Xiao, T., Xia, T., Yang, Y., Huang, C., Wang, X.: Learning from massive noisy labeled data for image classification. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2691\u20132699. IEEE, Boston, MA, USA (Jun 2015)"},{"issue":"7","key":"8_CR16","doi-asserted-by":"publisher","first-page":"1596","DOI":"10.1093\/jamia\/ocae108","volume":"31","author":"Y Wei","year":"2024","unstructured":"Wei, Y., Deng, Y., Sun, C., Lin, M., Jiang, H., Peng, Y.: Deep learning with noisy labels in medical prediction problems: a scoping review. J. Am. Med. Inform. Assoc. 31(7), 1596\u20131607 (2024). Jul","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"8_CR17","doi-asserted-by":"publisher","first-page":"21300","DOI":"10.1109\/ACCESS.2023.3249759","volume":"11","author":"F Yang","year":"2023","unstructured":"Yang, F., et al.: Assessing inter-annotator agreement for medical image segmentation. IEEE access\u202f: practical innovations, open solutions 11, 21300\u201321312 (2023)","journal-title":"IEEE access : practical innovations, open solutions"},{"key":"8_CR18","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1186\/s13244-023-01521-7","volume":"14","author":"L Zhang","year":"2023","unstructured":"Zhang, L., Wen, X., Li, J.W., Jiang, X., Yang, X.F., Li, M.: Diagnostic error and bias in the department of radiology: a pictorial essay. Insights Imaging 14, 163 (2023). Oct","journal-title":"Insights Imaging"}],"container-title":["Lecture Notes in Computer Science","Fairness of AI in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-05870-6_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T22:06:33Z","timestamp":1758233193000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05870-6_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,19]]},"ISBN":["9783032058690","9783032058706"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05870-6_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,19]]},"assertion":[{"value":"19 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"B.G. is part-time employee of DeepHealth. No other competing interests..","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"FAIMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"MICCAI Workshop on Fairness of AI in Medical Imaging","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":"23 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"faimi2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/faimi-workshop.github.io\/2025-miccai-workshop\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}