{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T17:03:33Z","timestamp":1779815013019,"version":"3.53.1"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031452482","type":"print"},{"value":"9783031452499","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-45249-9_26","type":"book-chapter","created":{"date-parts":[[2023,10,9]],"date-time":"2023-10-09T05:40:34Z","timestamp":1696830034000},"page":"266-275","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Unsupervised Bias Discovery in\u00a0Medical Image Segmentation"],"prefix":"10.1007","author":[{"given":"Nicol\u00e1s","family":"Gaggion","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rodrigo","family":"Echeveste","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lucas","family":"Mansilla","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Diego H.","family":"Milone","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Enzo","family":"Ferrante","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,10,9]]},"reference":[{"key":"26_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101797","volume":"66","author":"A Bustos","year":"2020","unstructured":"Bustos, A., Pertusa, A., Salinas, J.M., de la Iglesia-Vay\u00e1, M.: PadChest: a large chest x-ray image dataset with multi-label annotated reports. Med. Image Anal. 66, 101797 (2020)","journal-title":"Med. Image Anal."},{"issue":"2","key":"26_CR2","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1109\/TMI.2013.2290491","volume":"33","author":"S Candemir","year":"2014","unstructured":"Candemir, S., et al.: Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration. IEEE Trans. Med. Imaging 33(2), 577\u2013590 (2014). https:\/\/doi.org\/10.1109\/TMI.2013.2290491","journal-title":"IEEE Trans. Med. Imaging"},{"key":"26_CR3","doi-asserted-by":"crossref","unstructured":"Cubero, L., Serrano, J., Castelli, J., De Crevoisier, R., Acosta, O., Pascau, J.: Exploring uncertainty for clinical acceptability in head and neck deep learning-based oar segmentation. In: IEEE ISBI 2023. IEEE (2023)","DOI":"10.1109\/ISBI53787.2023.10230442"},{"key":"26_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"715","DOI":"10.1007\/978-3-030-78191-0_55","volume-title":"Information Processing in Medical Imaging","author":"S Czolbe","year":"2021","unstructured":"Czolbe, S., Arnavaz, K., Krause, O., Feragen, A.: Is segmentation uncertainty useful? In: Feragen, A., Sommer, S., Schnabel, J., Nielsen, M. (eds.) IPMI 2021. LNCS, vol. 12729, pp. 715\u2013726. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-78191-0_55"},{"key":"26_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102213","volume":"74","author":"J Fournel","year":"2021","unstructured":"Fournel, J., et al.: Medical image segmentation automatic quality control: a multi-dimensional approach. Med. Image Anal. 74, 102213 (2021)","journal-title":"Med. Image Anal."},{"key":"26_CR6","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.ejmp.2021.05.003","volume":"85","author":"Y Fu","year":"2021","unstructured":"Fu, Y., Lei, Y., Wang, T., Curran, W.J., Liu, T., Yang, X.: A review of deep learning based methods for medical image multi-organ segmentation. Physica Med. 85, 107\u2013122 (2021)","journal-title":"Physica Med."},{"key":"26_CR7","doi-asserted-by":"publisher","unstructured":"Gaggion, N., Mansilla, L., Mosquera, C., Milone, D.H., Ferrante, E.: Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis. IEEE Trans. Med. Imaging (2022). https:\/\/doi.org\/10.1109\/tmi.2022.3224660. https:\/\/doi.org\/10.1109","DOI":"10.1109\/tmi.2022.3224660"},{"key":"26_CR8","doi-asserted-by":"crossref","unstructured":"Gaggion, N., Vakalopoulou, M., Milone, D.H., Ferrante, E.: Multi-center anatomical segmentation with heterogeneous labels via landmark-based models. In: 20th IEEE International Symposium on Biomedical Imaging (ISBI). IEEE (2023)","DOI":"10.1109\/ISBI53787.2023.10230691"},{"issue":"2","key":"26_CR9","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1109\/TMI.2013.2284099","volume":"33","author":"S Jaeger","year":"2014","unstructured":"Jaeger, S., et al.: Automatic tuberculosis screening using chest radiographs. IEEE Trans. Med. Imaging 33(2), 233\u2013245 (2014). https:\/\/doi.org\/10.1109\/TMI.2013.2284099","journal-title":"IEEE Trans. Med. Imaging"},{"key":"26_CR10","doi-asserted-by":"crossref","unstructured":"Krishnakumar, A., Prabhu, V., Sudhakar, S., Hoffman, J.: UDIS: unsupervised discovery of bias in deep visual recognition models. In: British Machine Vision Conference (BMVC), vol.\u00a01, p.\u00a03 (2021)","DOI":"10.5244\/C.35.102"},{"key":"26_CR11","first-page":"728","volume":"33","author":"P Lahoti","year":"2020","unstructured":"Lahoti, P., et al.: Fairness without demographics through adversarially reweighted learning. Adv. Neural. Inf. Process. Syst. 33, 728\u2013740 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"23","key":"26_CR12","doi-asserted-by":"publisher","first-page":"12592","DOI":"10.1073\/pnas.1919012117","volume":"117","author":"AJ Larrazabal","year":"2020","unstructured":"Larrazabal, A.J., Nieto, N., Peterson, V., Milone, D.H., Ferrante, E.: Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis. Proc. Natl. Acad. Sci. 117(23), 12592\u201312594 (2020)","journal-title":"Proc. Natl. Acad. Sci."},{"issue":"5","key":"26_CR13","doi-asserted-by":"publisher","first-page":"e384","DOI":"10.1016\/S2589-7500(22)00003-6","volume":"4","author":"X Liu","year":"2022","unstructured":"Liu, X., Glocker, B., McCradden, M.M., Ghassemi, M., Denniston, A.K., Oakden-Rayner, L.: The medical algorithmic audit. Lancet Digit. Health 4(5), e384\u2013e397 (2022)","journal-title":"Lancet Digit. Health"},{"key":"26_CR14","unstructured":"Mansilla, L., Ferrante, E.: Segmentaci\u00f3n multi-atlas de im\u00e1genes m\u00e9dicas con selecci\u00f3n de atlas inteligente y control de calidad autom\u00e1tico. In: XXIV Congreso Argentino de Ciencias de la Computaci\u00f3n (La Plata, 2018) (2018)"},{"key":"26_CR15","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1016\/j.neunet.2020.01.023","volume":"124","author":"L Mansilla","year":"2020","unstructured":"Mansilla, L., Milone, D.H., Ferrante, E.: Learning deformable registration of medical images with anatomical constraints. Neural Netw. 124, 269\u2013279 (2020)","journal-title":"Neural Netw."},{"key":"26_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1007\/978-3-030-87199-4_39","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"E Puyol-Ant\u00f3n","year":"2021","unstructured":"Puyol-Ant\u00f3n, E., et al.: Fairness in cardiac MR image analysis: an investigation of bias due to data imbalance in deep learning based segmentation. In: de Bruijne, M., et al. (eds.) MICCAI 2021, Part III. LNCS, vol. 12903, pp. 413\u2013423. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87199-4_39"},{"issue":"1","key":"26_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-022-32186-3","volume":"13","author":"MA Ricci Lara","year":"2022","unstructured":"Ricci Lara, M.A., Echeveste, R., Ferrante, E.: Addressing fairness in artificial intelligence for medical imaging. Nat. Commun. 13(1), 1\u20136 (2022)","journal-title":"Nat. Commun."},{"key":"26_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015, Part III. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"26_CR19","first-page":"19304","volume":"35","author":"J Schrouff","year":"2022","unstructured":"Schrouff, J., Chen, C., et al.: Diagnosing failures of fairness transfer across distribution shift in real-world medical settings. Adv. Neural. Inf. Process. Syst. 35, 19304\u201319318 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"1","key":"26_CR20","doi-asserted-by":"publisher","first-page":"71","DOI":"10.2214\/ajr.174.1.1740071","volume":"174","author":"J Shiraishi","year":"2000","unstructured":"Shiraishi, J., et al.: Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists\u2019 detection of pulmonary nodules. Am. J. Roentgenol. 174(1), 71\u201374 (2000)","journal-title":"Am. J. Roentgenol."},{"issue":"8","key":"26_CR21","doi-asserted-by":"publisher","first-page":"1597","DOI":"10.1109\/TMI.2017.2665165","volume":"36","author":"VV Valindria","year":"2017","unstructured":"Valindria, V.V., et al.: Reverse classification accuracy: predicting segmentation performance in the absence of ground truth. IEEE Trans. Med. Imaging 36(8), 1597\u20131606 (2017)","journal-title":"IEEE Trans. Med. Imaging"}],"container-title":["Lecture Notes in Computer Science","Clinical Image-Based Procedures, Fairness of AI in Medical Imaging, and Ethical and Philosophical Issues in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-45249-9_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T16:14:41Z","timestamp":1779812081000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-45249-9_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031452482","9783031452499"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-45249-9_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"9 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"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":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"faimi2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/faimi-workshop.github.io\/2023-miccai\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}