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@ARTICLE{TayebiArasteh:977985,
      author       = {Tayebi Arasteh, Soroosh and Kuhl, Christiane and Sähn,
                      Marwin-Jonathan and Isfort, Peter and Truhn, Daniel and
                      Nebelung, Sven},
      title        = {{E}nhancing domain generalization in the {AI}-based
                      analysis of chest radiographs with federated learning},
      journal      = {Scientific reports},
      volume       = {13},
      issn         = {2045-2322},
      address      = {[London]},
      publisher    = {Macmillan Publishers Limited, part of Springer Nature},
      reportid     = {RWTH-2024-01043},
      pages        = {22576},
      year         = {2023},
      cin          = {951320 ; 951310 / 936210},
      ddc          = {600},
      cid          = {$I:(DE-82)951320_20220218$ / $I:(DE-82)532010-2_20140620$},
      pnm          = {OA - Open Access Publikation mit Unterstützung der
                      Universitätsbibliothek der RWTH Aachen University
                      (X021000-OA) / BMBF 01KX2021 - Nationales Forschungsnetzwerk
                      der Universitätsmedizin zu Covid-19 (BMBF-01KX2021) / BMBF
                      031L0312A - CompLS - Runde 5 - Verbundprojekt: TRANSFORM
                      LIVER - Weiterentwicklung von Vision Transformern zur
                      Entdeckung von Biomarkern bei Lebererkrankungen -
                      Teilprojekt A (031L0312A) / ODELIA - Open Consortium for
                      Decentralized Medical Artificial Intelligence (101057091) /
                      BMBF 01KD2215B - Verbund SWAG - SchWArmlernen und Generative
                      Modelle zur Synthese und Nutzbarmachung hochqualitativer
                      Daten in der Krebsmedizin - Standort Universitätsklinikum
                      Aachen (01KD2215B)},
      pid          = {G:(DE-82)X021000-OA / G:(DE-82)BMBF-01KX2021 /
                      G:(BMBF)031L0312A / G:(EU-Grant)101057091 /
                      G:(BMBF)01KD2215B},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001132995200024},
      pubmed       = {pmid:38114729},
      doi          = {10.1038/s41598-023-49956-8},
      url          = {https://publications.rwth-aachen.de/record/977985},
}