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DINOv3 extends earlier SSL models through Gram-anchored self-distillation and explicit high-resolution adaptation. Whether these changes improve transfer learning for chest radiograph classification has not been established.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>We benchmarked DINOv3 against DINOv2 and supervised ImageNet initialization across seven chest radiograph datasets comprising 816,183 radiographs from pediatric and adult cohorts. ViT-B\/16 and ConvNeXt-B were evaluated under full fine-tuning at 224 \u00d7 224 and 512 \u00d7 512 pixels, with targeted 1024 \u00d7 1024 experiments on three cohorts. Additional analyses examined parameter-efficient adaptation, synthetic label corruption, external validation, frozen 7B features, and computational efficiency. The primary outcome was the mean area under the receiver operating characteristic curve across labels.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>In adult cohorts, DINOv3 did not consistently outperform DINOv2 at 224 \u00d7 224 pixels, but became the strongest initialization at 512 \u00d7 512 pixels, especially with ConvNeXt-B. Gains were greatest for small focal and boundary-dependent abnormalities, whereas large-structure findings changed little. The pediatric cohort showed no significant benefit from DINOv3, higher resolution, or backbone choice. Scaling to 1024 \u00d7 1024 rarely improved performance and markedly increased computational cost. ConvNeXt-B remained superior to ViT-B\/16 under both full and parameter-efficient adaptation. External validation preserved the 512 \u00d7 512 DINOv3 advantage, whereas synthetic label corruption showed that this benefit should not be interpreted simply as superior noise robustness. Frozen DINOv3-7B features underperformed relative to fully adapted 86 to 89M-parameter backbones.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>For adult chest radiograph classification, DINOv3 provides its most reliable benefit at 512 \u00d7 512 pixels, particularly with ConvNeXt-B. Fully adapted mid-sized models at 512 \u00d7 512 pixels provided the best performance-cost trade-off in our benchmark.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1038\/s43856-026-01897-9","type":"journal-article","created":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T04:16:05Z","timestamp":1788927365000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Resolution-dependent self-supervised transfer in chest radiograph classification"],"prefix":"10.1038","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1015-7733","authenticated-orcid":false,"given":"Soroosh","family":"Tayebi Arasteh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1719-9944","authenticated-orcid":false,"given":"Mina","family":"Shaigan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christiane","family":"Kuhl","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3730-5348","authenticated-orcid":false,"given":"Jakob Nikolas","family":"Kather","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5267-9962","authenticated-orcid":false,"given":"Sven","family":"Nebelung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9605-0728","authenticated-orcid":false,"given":"Daniel","family":"Truhn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,9]]},"reference":[{"key":"1897_CR1","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1038\/s41591-021-01614-0","volume":"28","author":"P Rajpurkar","year":"2022","unstructured":"Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. 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S.T.A. is also an Editorial Board Member for\n                      European Radiology Experimental\n                      and a Trainee Editorial Board Member for\n                      Radiology: Artificial Intelligence\n                      . J.N.K. declares consulting services for Bioptimus, France; Owkin, France; DoMore Diagnostics, Norway; Panakeia, UK; AstraZeneca, UK; Scailyte, Switzerland; Mindpeak, Germany; and MultiplexDx, Slovakia. Furthermore, he holds shares in StratifAI GmbH, Germany, and in Synagen GmbH, Germany, has received a research grant by GSK, and has received honoraria by AstraZeneca, Bayer, Eisai, Janssen, MSD, BMS, Roche, Pfizer, and Fresenius. D.T. received honoraria for lectures by Bayer, GE, Roche, AstraZeneca, and Philips and holds shares in StratifAI GmbH, Germany, and in Synagen GmbH, Germany. The other authors do not have any competing interests to disclose.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Ethics"}}],"article-number":"479"}}