{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T07:47:10Z","timestamp":1758268030130,"version":"3.44.0"},"publisher-location":"Cham","reference-count":32,"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_11","type":"book-chapter","created":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T11:29:08Z","timestamp":1758194948000},"page":"104-114","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Predicting Patient Self-reported Race From Skin Histological Images with\u00a0Deep Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1556-0679","authenticated-orcid":false,"given":"Shengjia","family":"Chen","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4870-128X","authenticated-orcid":false,"given":"Ruchika","family":"Verma","sequence":"additional","affiliation":[]},{"given":"Kevin","family":"Clare","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8245-2366","authenticated-orcid":false,"given":"Jannes","family":"Jegminat","sequence":"additional","affiliation":[]},{"given":"Eugenia","family":"Alleva","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5537-5817","authenticated-orcid":false,"given":"Kuan-lin","family":"Huang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3934-9782","authenticated-orcid":false,"given":"Brandon","family":"Veremis","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7603-8687","authenticated-orcid":false,"given":"Thomas","family":"Fuchs","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6299-1311","authenticated-orcid":false,"given":"Gabriele","family":"Campanella","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,19]]},"reference":[{"key":"11_CR1","doi-asserted-by":"crossref","unstructured":"Adleberg, J., et al.: Predicting patient demographics from chest radiographs with deep learning. J. Am. Coll. Radiol. 19(10), 1151\u20131161 (2022)","DOI":"10.1016\/j.jacr.2022.06.008"},{"key":"11_CR2","unstructured":"Campanella, G., et\u00a0al.: A clinical benchmark of public self-supervised pathology foundation models. arXiv preprint arXiv:2407.06508 (2024)"},{"key":"11_CR3","unstructured":"Campanella, G., et\u00a0al.: Computational pathology at health system scale\u2013self-supervised foundation models from three billion images. arXiv preprint arXiv:2310.07033 (2023)"},{"issue":"3","key":"11_CR4","doi-asserted-by":"publisher","first-page":"850","DOI":"10.1038\/s41591-024-02857-3","volume":"30","author":"RJ Chen","year":"2024","unstructured":"Chen, R.J., et al.: Towards a general-purpose foundation model for computational pathology. Nat. Med. 30(3), 850\u2013862 (2024)","journal-title":"Nat. Med."},{"key":"11_CR5","unstructured":"Chen, S., et\u00a0al.: Benchmarking embedding aggregation methods in computational pathology: A clinical data perspective. arXiv preprint arXiv:2407.07841 (2024)"},{"key":"11_CR6","doi-asserted-by":"crossref","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)","DOI":"10.1038\/s41467-024-48666-7"},{"issue":"1","key":"11_CR7","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1038\/s41596-024-01047-2","volume":"20","author":"OS Nahhas","year":"2025","unstructured":"Nahhas, O.S., et al.: From whole-slide image to biomarker prediction: end-to-end weakly supervised deep learning in computational pathology. Nat. Protoc. 20(1), 293\u2013316 (2025)","journal-title":"Nat. Protoc."},{"issue":"6","key":"11_CR8","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":"11_CR9","doi-asserted-by":"crossref","unstructured":"Glocker, B., Jones, C., Roschewitz, M., Winzeck, S.: Risk of bias in chest radiography deep learning foundation models. Radiology: Artifi. Intell. 5(6), e230060 (2023)","DOI":"10.1148\/ryai.230060"},{"key":"11_CR10","unstructured":"Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249\u2013256. JMLR Workshop and Conference Proceedings (2010)"},{"key":"11_CR11","unstructured":"Harvey, V.M., et\u00a0al.: Integrating skin color assessments into clinical practice and research: a review of current approaches. J. Am. Acad. Dermatol. (2024)"},{"key":"11_CR12","doi-asserted-by":"crossref","unstructured":"Hill, B.G., Koback, F.L., Schilling, P.L.: The risk of shortcutting in deep learning algorithms for medical imaging research. Sci. Rep. 14(1), 29224 (2024)","DOI":"10.1038\/s41598-024-79838-6"},{"key":"11_CR13","doi-asserted-by":"crossref","unstructured":"Hosseini, M.S., et\u00a0al.: Computational pathology: a survey review and the way forward. J. Pathol. Inform. 100357 (2024)","DOI":"10.1016\/j.jpi.2023.100357"},{"key":"11_CR14","doi-asserted-by":"crossref","unstructured":"Howard, F.M., et al.: The impact of site-specific digital histology signatures on deep learning model accuracy and bias. Nat. Commun. 12(1), 4423 (2021)","DOI":"10.1038\/s41467-021-24698-1"},{"key":"11_CR15","unstructured":"Ilse, M., Tomczak, J., Welling, M.: Attention-based deep multiple instance learning. In: International Conference on Machine Learning, pp. 2127\u20132136. PMLR (2018)"},{"issue":"2","key":"11_CR16","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1038\/s42256-024-00797-8","volume":"6","author":"C Jones","year":"2024","unstructured":"Jones, C., Castro, D.C., Sousa Ribeiro, F., Oktay, O., McCradden, M., Glocker, B.: A causal perspective on dataset bias in machine learning for medical imaging. Nat. Mach. Intell. 6(2), 138\u2013146 (2024)","journal-title":"Nat. Mach. Intell."},{"key":"11_CR17","doi-asserted-by":"crossref","unstructured":"Kheiri, F., Rahnamayan, S., Makrehchi, M., Bidgoli, A.: Bias in histopathology datasets: a comprehensive investigation on possible factors (2024)","DOI":"10.21203\/rs.3.rs-4559295\/v1"},{"key":"11_CR18","unstructured":"Loshchilov, I., Hutter, F., et\u00a0al.: Fixing weight decay regularization in adam. arXiv preprint arXiv:1711.051015 (2017)"},{"issue":"6","key":"11_CR19","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1038\/s41551-020-00682-w","volume":"5","author":"MY Lu","year":"2021","unstructured":"Lu, M.Y., Williamson, D.F., Chen, T.Y., Chen, R.J., Barbieri, M., Mahmood, F.: Data-efficient and weakly supervised computational pathology on whole-slide images. Nat. Biomed. Eng. 5(6), 555\u2013570 (2021)","journal-title":"Nat. Biomed. Eng."},{"key":"11_CR20","doi-asserted-by":"crossref","unstructured":"McInnes, L., Healy, J., Melville, J.: Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426 (2018)","DOI":"10.21105\/joss.00861"},{"key":"11_CR21","doi-asserted-by":"crossref","unstructured":"Nazer, L.H., et al.: Bias in artificial intelligence algorithms and recommendations for mitigation. PLOS Digital Health 2(6), e0000278 (2023)","DOI":"10.1371\/journal.pdig.0000278"},{"key":"11_CR22","doi-asserted-by":"crossref","unstructured":"Oakden-Rayner, L., Dunnmon, J., Carneiro, G., R\u00e9, C.: Hidden stratification causes clinically meaningful failures in machine learning for medical imaging. In: Proceedings of the ACM Conference on Health, Inference, and Learning, pp. 151\u2013159 (2020)","DOI":"10.1145\/3368555.3384468"},{"key":"11_CR23","doi-asserted-by":"publisher","unstructured":"Pi\u00e7arra, C., Glocker, B.: Analysing race and sex bias in brain age prediction. In: Workshop on Clinical Image-Based Procedures, pp. 194\u2013204. Springer (2023). https:\/\/doi.org\/10.1007\/978-3-031-45249-9_19","DOI":"10.1007\/978-3-031-45249-9_19"},{"issue":"12","key":"11_CR24","doi-asserted-by":"publisher","first-page":"2176","DOI":"10.1038\/s41591-021-01595-0","volume":"27","author":"L Seyyed-Kalantari","year":"2021","unstructured":"Seyyed-Kalantari, L., Zhang, H., McDermott, M.B., Chen, I.Y., Ghassemi, M.: Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nat. Med. 27(12), 2176\u20132182 (2021)","journal-title":"Nat. Med."},{"issue":"12","key":"11_CR25","doi-asserted-by":"publisher","first-page":"930","DOI":"10.1038\/s44222-023-00096-8","volume":"1","author":"AH Song","year":"2023","unstructured":"Song, A.H., et al.: Artificial intelligence for digital and computational pathology. Nat. Rev. Bioeng. 1(12), 930\u2013949 (2023)","journal-title":"Nat. Rev. Bioeng."},{"issue":"4","key":"11_CR26","doi-asserted-by":"publisher","first-page":"1174","DOI":"10.1038\/s41591-024-02885-z","volume":"30","author":"A Vaidya","year":"2024","unstructured":"Vaidya, A., et al.: Demographic bias in misdiagnosis by computational pathology models. Nat. Med. 30(4), 1174\u20131190 (2024)","journal-title":"Nat. Med."},{"key":"11_CR27","unstructured":"Vorontsov, E., et\u00a0al.: A foundation model for clinical-grade computational pathology and rare cancers detection. Nat. Med., 1\u201312 (2024)"},{"key":"11_CR28","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1016\/j.humpath.2023.04.012","volume":"140","author":"KA Williams","year":"2023","unstructured":"Williams, K.A., Wondimu, B., Ajayi, A.M., Sokumbi, O.: Skin of color in dermatopathology: does color matter? Hum. Pathol. 140, 240\u2013266 (2023)","journal-title":"Hum. Pathol."},{"key":"11_CR29","unstructured":"Xu, H., et\u00a0al.: A whole-slide foundation model for digital pathology from real-world data. Nature, 1\u20138 (2024)"},{"key":"11_CR30","doi-asserted-by":"crossref","unstructured":"Yang, J., Soltan, A.A., Eyre, D.W., Yang, Y., Clifton, D.A.: An adversarial training framework for mitigating algorithmic biases in clinical machine learning. NPJ Digital Med. 6(1), 55 (2023)","DOI":"10.1038\/s41746-023-00805-y"},{"issue":"10","key":"11_CR31","doi-asserted-by":"publisher","first-page":"2838","DOI":"10.1038\/s41591-024-03113-4","volume":"30","author":"Y Yang","year":"2024","unstructured":"Yang, Y., Zhang, H., Gichoya, J.W., Katabi, D., Ghassemi, M.: The limits of fair medical imaging ai in real-world generalization. Nat. Med. 30(10), 2838\u20132848 (2024)","journal-title":"Nat. Med."},{"issue":"6654","key":"11_CR32","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1126\/science.adh4260","volume":"381","author":"J Zou","year":"2023","unstructured":"Zou, J., Gichoya, J.W., Ho, D.E., Obermeyer, Z.: Implications of predicting race variables from medical images. Science 381(6654), 149\u2013150 (2023)","journal-title":"Science"}],"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_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T22:04:23Z","timestamp":1758233063000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05870-6_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,19]]},"ISBN":["9783032058690","9783032058706"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05870-6_11","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":"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":"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"}}]}}