{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,22]],"date-time":"2025-06-22T11:40:03Z","timestamp":1750592403429,"version":"3.41.0"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031958373","type":"print"},{"value":"9783031958380","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-95838-0_35","type":"book-chapter","created":{"date-parts":[[2025,6,22]],"date-time":"2025-06-22T11:14:26Z","timestamp":1750590866000},"page":"355-364","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Unsupervised Domain Adaptation for Breast Cancer Detection in a Multi-Scanner Environment: A Case-Study from Norway"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8720-0288","authenticated-orcid":false,"given":"Alba","family":"Ordo\u00f1ez","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6835-0985","authenticated-orcid":false,"given":"Fredrik Andreas","family":"Dahl","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5010-4145","authenticated-orcid":false,"given":"Olav","family":"Brautaset","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-9758-5747","authenticated-orcid":false,"given":"Line","family":"Eikvil","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,23]]},"reference":[{"key":"35_CR1","doi-asserted-by":"publisher","first-page":"848","DOI":"10.3390\/diagnostics14080848","volume":"14","author":"A Carriero","year":"2024","unstructured":"Carriero, A., Groenhoff, L., Vologina, E., Basile, P., Albera, M.: Deep learning in breast cancer imaging: state of the art and recent advancements in early 2024. Diagnostics 14, 848 (2024)","journal-title":"Diagnostics"},{"key":"35_CR2","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1016\/S1470-2045(22)00194-2","volume":"23","author":"W Heindel","year":"2022","unstructured":"Heindel, W., et al.: TOSYMA Screening Trial Study Group: digital breast tomosynthesis plus synthesised mammography versus digital screening mammography for the detection of invasive breast cancer (TOSYMA): a multicentre, open-label, randomised, controlled, superiority trial. Lancet Oncol. 23, 601\u2013611 (2022)","journal-title":"Lancet Oncol."},{"key":"35_CR3","doi-asserted-by":"publisher","first-page":"102386","DOI":"10.1016\/j.artmed.2022.102386","volume":"132","author":"L Garrucho","year":"2022","unstructured":"Garrucho, L., Kushibar, K., Jouide, S., Diaz, O., Igual, L., Lekadir, K.: Domain generalization in deep learning-based mass detection in mammography: a large-scale multi-center study. Artif. Intell. Med. 132, 102386 (2022)","journal-title":"Artif. Intell. Med."},{"key":"35_CR4","doi-asserted-by":"publisher","first-page":"12495","DOI":"10.1038\/s41598-019-48995-4","volume":"9","author":"L Shen","year":"2019","unstructured":"Shen, L., Margolies, L.R., Rothstein, J.H., Fluder, E., McBride, R., Sieh, W.: Deep learning to improve breast cancer detection on screening mammography. Sci. Rep. 9, 12495 (2019)","journal-title":"Sci. Rep."},{"key":"35_CR5","doi-asserted-by":"publisher","first-page":"3435","DOI":"10.1109\/JBHI.2022.3153902","volume":"26","author":"R Gong","year":"2022","unstructured":"Gong, R., Han, X., Wang, J., Ying, S., Shi, J.: Self-supervised bi-channel transformer networks for computer-aided diagnosis. IEEE J. Biomed. Health Inform. 26, 3435\u20133446 (2022)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"35_CR6","doi-asserted-by":"crossref","unstructured":"Kumar, D., Kumar, C., Shao, M.: Cross-database mammographic image analysis through unsupervised domain adaptation. In: Proceedings of the 2017 IEEE International Conference on Big Data, pp. 4035\u20134042 (2017)","DOI":"10.1109\/BigData.2017.8258419"},{"key":"35_CR7","doi-asserted-by":"publisher","first-page":"170177","DOI":"10.1038\/sdata.2017.177","volume":"4","author":"RS Lee","year":"2017","unstructured":"Lee, R.S., Gimenez, F., Hoogi, A., Miyake, K.K., Gorovoy, M., Rubin, D.L.: A curated mammography data set for use in computer-aided detection and diagnosis research. Sci Data. 4, 170177 (2017)","journal-title":"Sci Data."},{"key":"35_CR8","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1016\/j.acra.2011.09.014","volume":"19","author":"IC Moreira","year":"2012","unstructured":"Moreira, I.C., Amaral, I., Domingues, I., Cardoso, A., Cardoso, M.J., Cardoso, J.S.: INbreast: toward a full-field digital mammographic database. Acad. Radiol. 19, 236\u2013248 (2012)","journal-title":"Acad. Radiol."},{"key":"35_CR9","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.neucom.2020.01.099","volume":"393","author":"R Shen","year":"2020","unstructured":"Shen, R., Yao, J., Yan, K., Tian, K., Jiang, C., Zhou, K.: Unsupervised domain adaptation with adversarial learning for mass detection in mammogram. Neurocomputing 393, 27\u201337 (2020)","journal-title":"Neurocomputing"},{"key":"35_CR10","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":"35_CR11","doi-asserted-by":"publisher","first-page":"2531","DOI":"10.1109\/TMI.2020.2973595","volume":"39","author":"L Zhang","year":"2020","unstructured":"Zhang, L., et al.: Generalizing deep learning for medical image segmentation to unseen domains via deep stacked transformation. IEEE Trans. Med. Imaging 39, 2531\u20132540 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"35_CR12","doi-asserted-by":"publisher","first-page":"106225","DOI":"10.1016\/j.cmpb.2021.106225","volume":"208","author":"G Modanwal","year":"2021","unstructured":"Modanwal, G., Vellal, A., Mazurowski, M.A.: Normalization of breast MRIs using cycle-consistent generative adversarial networks. Comput. Methods Programs Biomed. 208, 106225 (2021)","journal-title":"Comput. Methods Programs Biomed."},{"key":"35_CR13","doi-asserted-by":"crossref","unstructured":"Li, Z., Wu, J., Liu, W., Xia, Y.: Domain generalization for mammography detection via multi-style and multiview contrastive learning. In: Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2021, Lecture Notes in Computer Science, vol. 12907, pp. 98\u2013108. Springer, Cham (2021)","DOI":"10.1007\/978-3-030-87234-2_10"},{"key":"35_CR14","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1109\/TNNLS.2020.3028503","volume":"33","author":"S Zhao","year":"2020","unstructured":"Zhao, S., et al.: A review of single-source deep unsupervised visual domain adaptation. IEEE Trans. Neural Netw. Learn. Syst. 33, 473\u2013493 (2020)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"35_CR15","unstructured":"Long, M., CAO, Z., Wang, J., Jordan, M.I.: Conditional adversarial domain adaptation. In: Advances in Neural Information Processing Systems, pp. 1647\u20131657. Curran Associates, Inc., Montr\u00e9al (2018)"},{"key":"35_CR16","unstructured":"Long, M., Cao, Y., Wang, J., Jordan, M.: Learning transferable features with deep adaptation networks. In: Proceedings of the 2015 ICML, pp. 97\u2013105. PMLR, Lille (2015)"},{"key":"35_CR17","unstructured":"Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. In: Proceedings of the 2015 ICML, pp. 1180\u20131189. PLMR, Lille (2015)"},{"key":"35_CR18","doi-asserted-by":"crossref","unstructured":"Dahl, F., Brautaset, O., Holden, M., Eikvil, L., Larsen, M., Hofvind, S.: Two-stage mammography classification model using explainable-AI for ROI detection | Nordic Machine Intelligence (2023)","DOI":"10.5617\/nmi.10459"},{"key":"35_CR19","doi-asserted-by":"crossref","unstructured":"Busto, P.P., Gall, J.: Open set domain adaptation. In: Proceedings of the 2017 ICCV, pp. 754\u2013763. IEEE, Venice (2017)","DOI":"10.1109\/ICCV.2017.88"},{"key":"35_CR20","doi-asserted-by":"publisher","first-page":"1006","DOI":"10.1038\/s42256-023-00711-8","volume":"5","author":"R Achtibat","year":"2023","unstructured":"Achtibat, R., et al.: From attribution maps to human-understandable explanations through concept relevance propagation. Nat. Mach. Intell. 5, 1006\u20131019 (2023)","journal-title":"Nat. Mach. Intell."}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-95838-0_35","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,22]],"date-time":"2025-06-22T11:14:29Z","timestamp":1750590869000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-95838-0_35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031958373","9783031958380"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-95838-0_35","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"23 June 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"Authors state no conflict of interest.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"AIME","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Intelligence in Medicine","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pavia","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","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":"24 June 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 June 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aime2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aime25.aimedicine.info\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}