{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T17:17:48Z","timestamp":1788369468322,"version":"build-2803163510"},"reference-count":34,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computer Methods and Programs in Biomedicine"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.cmpb.2026.109596","type":"journal-article","created":{"date-parts":[[2026,8,8]],"date-time":"2026-08-08T15:06:37Z","timestamp":1786201597000},"page":"109596","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["EquiFL-X: Communication-constrained federated calibration via label-aggregated histograms for multi-view screening mammography under acquisition shift"],"prefix":"10.1016","volume":"286","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-2430-0163","authenticated-orcid":false,"given":"Batuhan","family":"\u00d6ney","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"3","key":"10.1016\/j.cmpb.2026.109596_b1","first-page":"229","article-title":"Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"74","author":"Bray","year":"2024","journal-title":"CA: Cancer J. Clin."},{"issue":"2","key":"10.1016\/j.cmpb.2026.109596_b2","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1148\/radiol.2019182627","article-title":"Artificial intelligence for mammography and digital breast tomosynthesis: Current concepts and future perspectives","volume":"293","author":"Geras","year":"2019","journal-title":"Radiology"},{"key":"10.1016\/j.cmpb.2026.109596_b3","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1109\/RBME.2024.3357877","article-title":"Deep learning in breast cancer imaging: A decade of progress and future directions","volume":"18","author":"Luo","year":"2025","journal-title":"IEEE Rev. Biomed. Eng."},{"issue":"1","key":"10.1016\/j.cmpb.2026.109596_b4","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1002\/int.22622","article-title":"A systematic survey of deep learning in breast cancer","volume":"37","author":"Yu","year":"2022","journal-title":"Int. J. Intell. Syst."},{"issue":"1","key":"10.1016\/j.cmpb.2026.109596_b5","doi-asserted-by":"crossref","first-page":"161","DOI":"10.3390\/diagnostics13010161","article-title":"Deep learning based methods for breast cancer diagnosis: A systematic review and future direction","volume":"13","author":"Nasser","year":"2023","journal-title":"Diagnostics"},{"key":"10.1016\/j.cmpb.2026.109596_b6","doi-asserted-by":"crossref","first-page":"1913","DOI":"10.1007\/s11831-025-10359-9","article-title":"A comprehensive examination of machine learning and deep learning approaches for breast cancer detection, classification, segmentation, augmentation, and feature selection","volume":"33","author":"Majidpour","year":"2026","journal-title":"Arch. Comput. Methods Eng."},{"issue":"7788","key":"10.1016\/j.cmpb.2026.109596_b7","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1038\/s41586-019-1799-6","article-title":"International evaluation of an AI system for breast cancer screening","volume":"577","author":"McKinney","year":"2020","journal-title":"Nature"},{"issue":"4","key":"10.1016\/j.cmpb.2026.109596_b8","doi-asserted-by":"crossref","first-page":"1184","DOI":"10.1109\/TMI.2019.2945514","article-title":"Deep neural networks improve radiologists\u2019 performance in breast cancer screening","volume":"39","author":"Wu","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"3","key":"10.1016\/j.cmpb.2026.109596_b9","doi-asserted-by":"crossref","first-page":"e138","DOI":"10.1016\/S2589-7500(20)30003-0","article-title":"Changes in cancer detection and false-positive recall in mammography using artificial intelligence: A retrospective, multireader study","volume":"2","author":"Kim","year":"2020","journal-title":"Lancet Digit. Health"},{"issue":"1","key":"10.1016\/j.cmpb.2026.109596_b10","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1148\/radiol.2019182716","article-title":"A deep learning mammography-based model for improved breast cancer risk prediction","volume":"292","author":"Yala","year":"2019","journal-title":"Radiology"},{"issue":"6","key":"10.1016\/j.cmpb.2026.109596_b11","doi-asserted-by":"crossref","first-page":"e406","DOI":"10.1016\/S2589-7500(22)00063-2","article-title":"AI recognition of patient race in medical imaging: A modelling study","volume":"4","author":"Gichoya","year":"2022","journal-title":"Lancet Digit. Health"},{"issue":"1","key":"10.1016\/j.cmpb.2026.109596_b12","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1148\/radiol.2019182908","article-title":"A deep learning model to triage screening mammograms: A simulation study","volume":"293","author":"Yala","year":"2019","journal-title":"Radiology"},{"key":"10.1016\/j.cmpb.2026.109596_b13","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.csbj.2025.03.031","article-title":"Towards generalizable federated learning in medical imaging: A real-world case study on mammography data","volume":"28","author":"Tzortzis","year":"2025","journal-title":"Comput. Struct. Biotechnol. J."},{"issue":"1","key":"10.1016\/j.cmpb.2026.109596_b14","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1038\/s41746-020-00323-1","article-title":"The future of digital health with federated learning","volume":"3","author":"Rieke","year":"2020","journal-title":"Npj Digit. Med."},{"issue":"1","key":"10.1016\/j.cmpb.2026.109596_b15","doi-asserted-by":"crossref","first-page":"12598","DOI":"10.1038\/s41598-020-69250-1","article-title":"Federated learning in medicine: Facilitating multi-institutional collaborations without sharing patient data","volume":"10","author":"Sheller","year":"2020","journal-title":"Sci. Rep."},{"issue":"10","key":"10.1016\/j.cmpb.2026.109596_b16","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1038\/s41591-021-01506-3","article-title":"Federated learning for predicting clinical outcomes in patients with COVID-19","volume":"27","author":"Dayan","year":"2021","journal-title":"Nature Med."},{"issue":"3","key":"10.1016\/j.cmpb.2026.109596_b17","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1109\/MSP.2020.2975749","article-title":"Federated learning: Challenges, methods, and future directions","volume":"37","author":"Li","year":"2020","journal-title":"IEEE Signal Process. Mag."},{"key":"10.1016\/j.cmpb.2026.109596_b18","series-title":"Proceedings of the 36th International Conference on Machine Learning","first-page":"4615","article-title":"Agnostic federated learning","volume":"vol. 97","author":"Mohri","year":"2019"},{"issue":"1","key":"10.1016\/j.cmpb.2026.109596_b19","doi-asserted-by":"crossref","DOI":"10.1016\/j.patter.2023.100907","article-title":"Unified fair federated learning for digital healthcare","volume":"5","author":"Zhang","year":"2024","journal-title":"Patterns"},{"issue":"1","key":"10.1016\/j.cmpb.2026.109596_b20","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1007\/s44163-025-00426-2","article-title":"Advanced deep learning architectures for enhanced mammography classification: A comparative study of CNNs and ViT","volume":"5","author":"Sharma","year":"2025","journal-title":"Discov. Artif. Intell."},{"issue":"12","key":"10.1016\/j.cmpb.2026.109596_b21","doi-asserted-by":"crossref","first-page":"2176","DOI":"10.1038\/s41591-021-01595-0","article-title":"Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations","volume":"27","author":"Seyyed-Kalantari","year":"2021","journal-title":"Nature Med."},{"issue":"1","key":"10.1016\/j.cmpb.2026.109596_b22","doi-asserted-by":"crossref","DOI":"10.1136\/bmjhci-2023-100954","article-title":"Explainable machine learning for breast cancer diagnosis from mammography and ultrasound images: A systematic review","volume":"31","author":"Gurmessa","year":"2024","journal-title":"BMJ Health & Care Informatics"},{"key":"10.1016\/j.cmpb.2026.109596_b23","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"10124","article-title":"Post-hoc uncertainty calibration for domain drift scenarios","author":"Tomani","year":"2021"},{"key":"10.1016\/j.cmpb.2026.109596_b24","series-title":"Proceedings of the 37th International Conference on Machine Learning","first-page":"1597","article-title":"A simple framework for contrastive learning of visual representations","volume":"vol. 119","author":"Chen","year":"2020"},{"key":"10.1016\/j.cmpb.2026.109596_b25","series-title":"Advances in Neural Information Processing Systems","first-page":"21271","article-title":"Bootstrap your own latent: A new approach to self-supervised learning","volume":"Vol. 33","author":"Grill","year":"2020"},{"key":"10.1016\/j.cmpb.2026.109596_b26","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"16000","article-title":"Masked autoencoders are scalable vision learners","author":"He","year":"2022"},{"issue":"1","key":"10.1016\/j.cmpb.2026.109596_b27","doi-asserted-by":"crossref","first-page":"4581","DOI":"10.1038\/s41467-022-32186-3","article-title":"Addressing fairness in artificial intelligence for medical imaging","volume":"13","author":"Ricci Lara","year":"2022","journal-title":"Nat. Commun."},{"issue":"5","key":"10.1016\/j.cmpb.2026.109596_b28","doi-asserted-by":"crossref","first-page":"542","DOI":"10.3390\/diagnostics14050542","article-title":"Ultrasound image analysis with vision transformers\u2014Review","volume":"14","author":"Vafaeezadeh","year":"2024","journal-title":"Diagnostics"},{"key":"10.1016\/j.cmpb.2026.109596_b29","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"7167","article-title":"Adversarial discriminative domain adaptation","author":"Tzeng","year":"2017"},{"key":"10.1016\/j.cmpb.2026.109596_b30","series-title":"International Conference on Learning Representations","article-title":"Fair resource allocation in federated learning","author":"Li","year":"2020"},{"issue":"1","key":"10.1016\/j.cmpb.2026.109596_b31","doi-asserted-by":"crossref","first-page":"1479","DOI":"10.1038\/s41597-025-05866-0","article-title":"Full field digital mammography dataset from a population screening program","volume":"12","author":"Kendall","year":"2025","journal-title":"Sci. Data"},{"key":"10.1016\/j.cmpb.2026.109596_b32","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"8064","article-title":"Learning federated visual prompt in null space for MRI reconstruction","author":"Feng","year":"2023"},{"key":"10.1016\/j.cmpb.2026.109596_b33","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"23287","article-title":"Towards instance-adaptive inference for federated learning","author":"Feng","year":"2023"},{"key":"10.1016\/j.cmpb.2026.109596_b34","doi-asserted-by":"crossref","first-page":"714","DOI":"10.1038\/s41746-025-02085-0","article-title":"From pretraining to privacy: Federated ultrasound foundation model with self-supervised learning","volume":"8","author":"Jiang","year":"2025","journal-title":"Npj Digit. Med."}],"container-title":["Computer Methods and Programs in Biomedicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260726003457?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260726003457?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T16:59:28Z","timestamp":1788368368000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0169260726003457"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":34,"alternative-id":["S0169260726003457"],"URL":"https:\/\/doi.org\/10.1016\/j.cmpb.2026.109596","relation":{},"ISSN":["0169-2607"],"issn-type":[{"value":"0169-2607","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"EquiFL-X: Communication-constrained federated calibration via label-aggregated histograms for multi-view screening mammography under acquisition shift","name":"articletitle","label":"Article Title"},{"value":"Computer Methods and Programs in Biomedicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cmpb.2026.109596","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"109596"}}