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@ARTICLE{Heinz:843936,
      author       = {Heinz, Céline N. and Echle, Amelie and Foersch, Sebastian
                      and Bychkov, Andrey and Kather, Jakob N.},
      title        = {{T}he future of artificial intelligence in digital
                      pathology – results of a survey across stakeholder groups},
      journal      = {Histopathology : journal of the British Division of the
                      International Academy of Pathology},
      volume       = {80},
      number       = {7},
      issn         = {0309-0167},
      address      = {Oxford [u.a.]},
      publisher    = {Wiley-Blackwell},
      reportid     = {RWTH-2022-03527},
      pages        = {1121-1127},
      year         = {2022},
      cin          = {531030-2},
      ddc          = {610},
      cid          = {$I:(DE-82)531030-2_20140620$},
      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) / Max-Eder-Nachwuchsgruppenprogramm -
                      Optimierung von Immuntherapie-Strategien bei
                      gastrointestinalen Tumoren mit prädiktiven und
                      mechanistischen Computermodellen
                      (Stiftung-Deutsche-Krebshilfe-70113864)},
      pid          = {G:(DE-82)X021000-OA / G:(DE-82)BMG-ZMVI1-2520DAT111 /
                      G:(DE-82)Stiftung-Deutsche-Krebshilfe-70113864},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000793547800001},
      doi          = {10.1111/his.14659},
      url          = {https://publications.rwth-aachen.de/record/843936},
}