{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T12:10:48Z","timestamp":1773490248150,"version":"3.50.1"},"publisher-location":"Wiesbaden","reference-count":17,"publisher":"Springer Fachmedien Wiesbaden","isbn-type":[{"value":"9783658510992","type":"print"},{"value":"9783658511005","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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\/978-3-658-51100-5_29","type":"book-chapter","created":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T14:06:50Z","timestamp":1773238010000},"page":"133-138","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Impact of Preprocessing Methods on Racial Encoding and Model Robustness in CXR Diagnosis"],"prefix":"10.1007","author":[{"given":"Dishantkumar","family":"Sutariya","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0097-3868","authenticated-orcid":false,"given":"Eike","family":"Petersen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,3,12]]},"reference":[{"key":"29_CR1","doi-asserted-by":"crossref","unstructured":"Seyyed-Kalantari L, Zhang H, McDermottMBA, Chen IY, Ghassemi M. Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Lancet Digit Health. 2021;27(12):2176\u201382.","DOI":"10.1038\/s41591-021-01595-0"},{"key":"29_CR2","doi-asserted-by":"crossref","unstructured":"Gichoya JW et al. AI recognition of patient race in medical imaging: a modelling study. Lancet Digit Health. 2022;4(6):e406\u2013e414.","DOI":"10.1016\/S2589-7500(22)00063-2"},{"key":"29_CR3","doi-asserted-by":"crossref","unstructured":"Burns JL et al. Ability of artificial intelligence to identify self-reported race in chest X-ray using pixel intensity counts. J Med Imaging. 2023;10(6):061106.","DOI":"10.1117\/1.JMI.10.6.061106"},{"key":"29_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. 2023;89:104467.","DOI":"10.1016\/j.ebiom.2023.104467"},{"key":"29_CR5","doi-asserted-by":"crossref","unstructured":"Glocker B, Jones C, Roschewitz M, Winzeck S. Risk of bias in chest radiography deep learning foundation models. Radiol Artif Intell. 2023;5(6).","DOI":"10.1148\/ryai.230060"},{"key":"29_CR6","doi-asserted-by":"crossref","unstructured":"Lotter W. Acquisition parameters influence AI recognition of race in chest X-rays and mitigating these factors reduces underdiagnosis bias. Nat Commun. 2024;15(1):7465.","DOI":"10.1038\/s41467-024-52003-3"},{"key":"29_CR7","doi-asserted-by":"crossref","unstructured":"Wang R, Kuo PC, Chen LC, Seastedt KP, Gichoya JW, Celi LA. Drop the shortcuts: image augmentation improves fairness and decreases AI detection of race and other demographics from medical images. EBioMedicine. 2024;102:105047.","DOI":"10.1016\/j.ebiom.2024.105047"},{"key":"29_CR8","doi-asserted-by":"crossref","unstructured":"Johnson AEW, Pollard TJ, Berkowitz SJ, Greenbaum NR, Lungren MP et al. MIMICCXR: a de-identified publicly available database of chest radiographs with free-text reports. Sci Data. 2019;6(1):317.","DOI":"10.1038\/s41597-019-0322-0"},{"key":"29_CR9","unstructured":"Johnson A, Lungren M, Peng Y, Lu Z, Mark R, Berkowitz S et al. MIMIC-CXR-JPG: chest radiographs with structured labels. PhysioNet, 2024."},{"key":"29_CR10","doi-asserted-by":"crossref","unstructured":"Weng N, Bigdeli S, Petersen E, Feragen A. Are sex-based physiological differences the cause of gender bias for chest X-ray diagnosis? Proc FAIMI. 2023:142\u201352.","DOI":"10.1007\/978-3-031-45249-9_14"},{"key":"29_CR11","doi-asserted-by":"crossref","unstructured":"Irvin J, Rajpurkar P, Ko M, Yu Y, Ciurea-Ilcus S, Chute C et al. CheXpert: a large chest radiograph dataset with uncertainty labels and expert comparison. Proc AAAI CAI. 2019;33(1):590\u20137.","DOI":"10.1609\/aaai.v33i01.3301590"},{"key":"29_CR12","doi-asserted-by":"crossref","unstructured":"Zuiderveld KJ et al. Contrast limited adaptive histogram equalization. Graph Gems. 1994;4(1):474\u201385.","DOI":"10.1016\/B978-0-12-336156-1.50061-6"},{"key":"29_CR13","doi-asserted-by":"crossref","unstructured":"Aslani S, Lilaonitkul W, Gnanananthan V, Raj D et al. Optimising chest X-rays for image analysis by identifying and removing confounding factors. Proc MICAD. 2022:245\u201354.","DOI":"10.1007\/978-981-16-6775-6_20"},{"key":"29_CR14","doi-asserted-by":"crossref","unstructured":"Sourget T, Hestbek-M\u00f8ller M, Jim\u00e9nez-S\u00e1nchez A, Junchi Xu J, Cheplygina V. Mask of truth: model sensitivity to unexpected regions of medical images. J Imaging Inform Med. 2025.","DOI":"10.1007\/s10278-025-01531-5"},{"key":"29_CR15","doi-asserted-by":"crossref","unstructured":"Gaggion N, Mosquera C, Mansilla L, Saidman JM, Aineseder M, Milone DH et al. CheXmask: a large-scale dataset of anatomical segmentation masks for multi-center chest X-ray images. Sci Data. 2024;11(1):511.","DOI":"10.1038\/s41597-024-03358-1"},{"key":"29_CR16","doi-asserted-by":"crossref","unstructured":"Petersen E, Holm S, Ganz M, Feragen A. The path toward equal performance in medical machine learning. Patterns. 2023;4(7).","DOI":"10.1016\/j.patter.2023.100790"},{"key":"29_CR17","doi-asserted-by":"crossref","unstructured":"Liu G, Reda FA, Shih KJ, Wang TC, Tao A, Catanzaro B. Image inpainting for irregular holes using partial convolutions. Proc ECCV. 2018:85\u2013100.","DOI":"10.1007\/978-3-030-01252-6_6"}],"container-title":["Informatik aktuell","Bildverarbeitung f\u00fcr die Medizin 2026"],"original-title":[],"language":"de","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-658-51100-5_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T11:05:35Z","timestamp":1773486335000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-658-51100-5_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783658510992","9783658511005"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-658-51100-5_29","relation":{},"ISSN":["1431-472X","2628-8958"],"issn-type":[{"value":"1431-472X","type":"print"},{"value":"2628-8958","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"12 March 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BVM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"BVM Workshop","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"L\u00fcbeck","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Deutschland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 March 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 March 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bvm2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.bvm-conf.org\/de\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}