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@ARTICLE{Saldanha:855936,
      author       = {Saldanha, Oliver Lester and Muti, Hannah Sophie and
                      Grabsch, Heike I. and Langer, Rupert and Dislich, Bastian
                      and Kohlruss, Meike and Keller, Gisela and van Treeck, Marko
                      and Hewitt, Katherine Jane and Kolbinger, Fiona R. and
                      Veldhuizen, Gregory Patrick and Boor, Peter and Foersch,
                      Sebastian and Truhn, Daniel and Kather, Jakob Nikolas},
      title        = {{D}irect prediction of genetic aberrations from pathology
                      images in gastric cancer with swarm learning},
      journal      = {Gastric cancer},
      volume       = {26},
      number       = {2},
      issn         = {1436-3291},
      address      = {Tokyo},
      publisher    = {Springer},
      reportid     = {RWTH-2022-10557},
      pages        = {264-274},
      year         = {2022},
      note         = {Published: 20 October 2022},
      cin          = {531030-6 / 528001-2 / 531020-2 / 532010-2},
      ddc          = {610},
      cid          = {$I:(DE-82)531030-6_20220506$ / $I:(DE-82)528001-2_20140620$
                      / $I:(DE-82)531020-2_20140620$ /
                      $I:(DE-82)532010-2_20140620$},
      pnm          = {OA - Open Access Publikation mit Unterstützung der
                      Universitätsbibliothek der RWTH Aachen University
                      (X021000-OA) / BMG-ZMVI1-2520DAT111 - Diagnosestellung und
                      Risikostratifizierung von Lebererkrankungen mittels Deep
                      Learning anhand von klinischen Routinedaten (DEEP LIVER)
                      (BMG-ZMVI1-2520DAT111) / DFG project 322900939 - TRR 219:
                      Mechanismen kardiovaskulärer Komplikationen bei chronischer
                      Niereninsuffizienz (322900939) / DFG project 454024652 -
                      Translationale Nephropathologie (454024652) / DFG project
                      432698239 - Die Rolle von epithelialen CD74 in
                      Nierenerkrankungen (432698239) / DFG project 445703531 - KFO
                      5011: Integration neuer Methoden zur Verbesserung von
                      translationaler Nierenforschung (445703531) /
                      AIM.imaging.CKD - AI-augmented, Multiscale Image-based
                      Diagnostics of Chronic Kidney Disease (101001791) / BMBF
                      01GM1901A - STOP-FSGS - Translationaler Forschungsverbund
                      zur Verbesserung der Diagnostik und Therapie der FSGS, TP 1:
                      Rolle des MIF-Signalwegs bei FSGS; TP 4: Pathogenese und
                      neue therapeutische Ansätze (01GM1901A) / Verbundprojekt:
                      EMPAIA – Ecosystem for pathology diagnostics with AI
                      assistance; Teilvorhaben: Koordination, Referenzzentren und
                      Workflowintegragtion von KI-Lösungen (01MK2002A)},
      pid          = {G:(DE-82)X021000-OA / G:(DE-82)BMG-ZMVI1-2520DAT111 /
                      G:(GEPRIS)322900939 / G:(GEPRIS)454024652 /
                      G:(GEPRIS)432698239 / G:(GEPRIS)445703531 /
                      G:(EU-Grant)101001791 / G:(BMBF)01GM1901A /
                      G:(BMWK)01MK2002A},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000870673500001},
      doi          = {10.1007/s10120-022-01347-0},
      url          = {https://publications.rwth-aachen.de/record/855936},
}