{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T18:16:18Z","timestamp":1787940978381,"version":"build-2784847793"},"reference-count":42,"publisher":"Oxford University Press (OUP)","issue":"8","license":[{"start":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T00:00:00Z","timestamp":1785196800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundations of China","doi-asserted-by":"crossref","award":["62272364"],"award-info":[{"award-number":["62272364"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Provincial Key Research and Development Program of Shaanxi","award":["2024GH-ZDXM-47"],"award-info":[{"award-number":["2024GH-ZDXM-47"]}]},{"name":"China Education Society of Electronics Teaching Reform","award":["DJZ25005"],"award-info":[{"award-number":["DJZ25005"]}]},{"name":"Shaanxi Higher Education Teaching Reform Research","award":["25BY055"],"award-info":[{"award-number":["25BY055"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,8,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Radiology reports play a pivotal role in guiding treatment planning and enabling effective doctor-patient communication. However, their manual composition imposes a substantial workload on radiologists. Although automatic radiology report generation has emerged as a promising alternative, existing approaches predominantly rely on single-view chest X-rays and fail to adequately leverage patient-specific context, thereby limiting diagnostic accuracy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>To address this challenge, we propose EVOKE, a novel chest X-ray report generation framework that incorporates multi-view contrastive learning and patient-specific knowledge. Specifically, we introduce a multi-view contrastive learning method that captures semantic correspondences both among multi-view radiographs within a study and between these radiographs and their associated report, thereby improving visual representation learning. We further present a knowledge-guided report generation module that integrates available patient-specific knowledge (i.e. indication, which includes symptom descriptions) to facilitate the generation of accurate and coherent radiology reports. To support research in multi-view report generation, we construct Multi-view CXR and Two-view CXR datasets using publicly available sources. Our proposed EVOKE surpasses recent state-of-the-art methods across multiple datasets, achieving a 2.9% F1 RadGraph improvement on MIMIC-CXR, a 5.0% BLEU-1 improvement on MIMIC-ABN, a 1.5% BLEU-4 improvement on Multi-view CXR, and an 8.2% F1,mic-14 CheXbert improvement on Two-view CXR.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability<\/jats:title>\n                    <jats:p>Code is publicly available at https:\/\/github.com\/mk-runner\/EVOKE, with an archived release available on Zenodo (doi:10.5281\/zenodo.21000219).<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag566","type":"journal-article","created":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T11:22:33Z","timestamp":1785151353000},"source":"Crossref","is-referenced-by-count":0,"title":["Patient-specific multimodal learning with multi-view contrastive alignment for chest X-ray report 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Shaanxi 710071,","place":["China"]},{"name":"Key Laboratory of Smart Education of Shaanxi Higher Education Institutes, Xidian University , Xi\u2019an, Shaanxi 710071,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0729-9706","authenticated-orcid":false,"given":"Zhuoqi","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xidian University , Xi\u2019an, Shaanxi 710071,","place":["China"]},{"name":"Xi\u2019an Key Laboratory of Big Data and Intelligent Vision , Xi\u2019an, Shaanxi 710071,","place":["China"]},{"name":"Key Laboratory of Smart Education of Shaanxi Higher Education Institutes, Xidian University , Xi\u2019an, Shaanxi 710071,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7316-4354","authenticated-orcid":false,"given":"Yunan","family":"Li","sequence":"additional","affiliation":[{"name":"School 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