{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:10:06Z","timestamp":1767319806199,"version":"3.48.0"},"publisher-location":"Cham","reference-count":10,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032095688","type":"print"},{"value":"9783032095695","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\/978-3-032-09569-5_37","type":"book-chapter","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:07:04Z","timestamp":1767319624000},"page":"370-378","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Bias-Resilient Feature Learning for Robust Domain Adaptation in Mammography"],"prefix":"10.1007","author":[{"given":"Degan","family":"Hao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dooman","family":"Arefan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Margarita L.","family":"Zuley","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Na","family":"Du","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shandong","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"key":"37_CR1","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1038\/s41591-021-01287-9","volume":"27","author":"M Gehrung","year":"2021","unstructured":"Gehrung, M., Crispin-Ortuzar, M., Berman, A.G., O\u2019Donovan, M., Fitzgerald, R.C., Markowetz, F.: Triage-driven diagnosis of Barrett\u2019s esophagus for early detection of esophageal adenocarcinoma using deep learning. Nat. Med. 27, 833\u2013841 (2021)","journal-title":"Nat. Med."},{"key":"37_CR2","doi-asserted-by":"publisher","first-page":"1519","DOI":"10.1038\/s41591-019-0583-3","volume":"25","author":"P Courtiol","year":"2019","unstructured":"Courtiol, P., et al.: Deep learning-based classification of mesothelioma improves prediction of patient outcome. Nat. Med. 25, 1519\u20131525 (2019)","journal-title":"Nat. Med."},{"key":"37_CR3","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1038\/s41746-021-00416-5","volume":"4","author":"R Zeleznik","year":"2021","unstructured":"Zeleznik, R., et al.: Deep-learning system to improve the quality and efficiency of volumetric heart segmentation for breast cancer. NPJ Digital Med. 4, 43 (2021)","journal-title":"NPJ Digital Med."},{"key":"37_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102147","volume":"73","author":"Y Wang","year":"2021","unstructured":"Wang, Y., Feng, Y., Zhang, L., Wang, Z., Lv, Q., Yi, Z.: Deep adversarial domain adaptation for breast cancer screening from mammograms. Med. Image Anal. 73, 102147 (2021)","journal-title":"Med. Image Anal."},{"key":"37_CR5","first-page":"19304","volume":"35","author":"J Schrouff","year":"2022","unstructured":"Schrouff, J., 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."},{"key":"37_CR6","doi-asserted-by":"crossref","unstructured":"Motiian, S., Piccirilli, M., Adjeroh, D.A., Doretto, G.: Unified deep supervised domain adaptation and generalization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5715\u20135725 (2017)","DOI":"10.1109\/ICCV.2017.609"},{"key":"37_CR7","unstructured":"Cui, C., et al.: The Chinese mammography database (CMMD): an online mammography database with biopsy confirmed types for machine diagnosis of breast. Cancer Imaging Archive 1 (2021)"},{"key":"37_CR8","doi-asserted-by":"crossref","unstructured":"Wang, A., Russakovsky, O.: Overwriting pretrained bias with finetuning data. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3957\u20133968 (2023)","DOI":"10.1109\/ICCV51070.2023.00366"},{"key":"37_CR9","unstructured":"Hao, D., Arefan, D., Zuley, M., Berg, W., Wu, S.: Adversarially robust feature learning for breast cancer diagnosis. arXiv preprint arXiv:2402.08768 (2024)"},{"key":"37_CR10","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"}],"container-title":["Lecture Notes in Computer Science","Applications of Medical Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-09569-5_37","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:07:05Z","timestamp":1767319625000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-09569-5_37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032095688","9783032095695"],"references-count":10,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-09569-5_37","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"2 January 2026","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":"AMAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Applications of Medical AI","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":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"amai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/amai2025\/home","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}