{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T13:59:02Z","timestamp":1791899942095,"version":"4.3.4"},"reference-count":43,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,5,31]],"date-time":"2026-05-31T00:00:00Z","timestamp":1780185600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004054","name":"King Abdulaziz University","doi-asserted-by":"publisher","award":["IPP: 1259-830-2025"],"award-info":[{"award-number":["IPP: 1259-830-2025"]}],"id":[{"id":"10.13039\/501100004054","id-type":"DOI","asserted-by":"publisher"}]},{"award":["IPP: 1259-830-2025"],"award-info":[{"award-number":["IPP: 1259-830-2025"]}],"id":[{"id":"https:\/\/ror.org\/02ma4wv74","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>While promising results have been demonstrated for deep learning-based breast cancer diagnosis using mammography, problems persist in approaches that rely primarily on visual information. These problems include inadequate performance across diverse clinical centers, various imaging protocols, scanner types, and patient distributions. Here, we introduce Cross-Center Vision\u2013Language Transformer (CC-VLT), a framework that integrates mammograms and clinical text to enable more robust, guided diagnosis. The framework incorporates a vision transformer for mammograms, a text transformer for salient clinical descriptors, bi-directional cross-modal attention for semantics, and a cross-center feature regularization approach to address the challenge of inter-institutional domain shifts. The framework is tested on a leave-one-center-out basis across several public mammography datasets and significantly outperforms strong baseline models in both intra- and cross-center evaluations. Our framework achieved an accuracy of 90.7% with an intra-center ROC\u2013AUC of 0.951 and cross-center ROC\u2013AUC results of 0.912, 0.927, and 0.934 on the CBIS-DDSM, INbreast, and VinDr-Mammo datasets, respectively. Reliability of the malignancy probability predictions improved, as evidenced by a diminished Expected Calibration Error and Brier Score. Our framework, by designing an effective integrated vision\u2013language interaction model and implementing a cross-center feature regularization approach, sets a benchmark for robust breast cancer diagnosis across diverse clinical environments.<\/jats:p>","DOI":"10.3390\/bioengineering13060653","type":"journal-article","created":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T07:58:38Z","timestamp":1780473518000},"page":"653","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Cross-Center Vision\u2013Language Transformer for Robust Mammography-Based Breast Cancer Diagnosis"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3277-4505","authenticated-orcid":false,"given":"Anas W.","family":"Abulfaraj","sequence":"first","affiliation":[{"name":"Department of Information Systems, King Abdulaziz University, P.O. Box 344, Rabigh 21911, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,31]]},"reference":[{"key":"ref_1","first-page":"394","article-title":"Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"68","author":"Bray","year":"2018","journal-title":"CA Cancer J. Clin."},{"key":"ref_2","first-page":"17","article-title":"Cancer statistics, 2023","volume":"73","author":"Siegel","year":"2023","journal-title":"CA Cancer J. 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