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@ARTICLE{Saldanha:179696,
      author       = {O. L. Saldanha and P. Quirke and N. P. West and J. A. James
                      and M. B. Loughrey and H. I. Grabsch and M. Salto-Tellez and
                      E. Alwers$^*$ and D. Cifci and N. Ghaffari Laleh and T.
                      Seibel and R. Gray and G. G. A. Hutchins and H. Brenner$^*$
                      and M. van Treeck and T. Yuan$^*$ and T. J. Brinker$^*$ and
                      J. Chang-Claude$^*$ and F. Khader and A. Schuppert and T.
                      Luedde and C. Trautwein and H. S. Muti and S. Foersch and M.
                      Hoffmeister$^*$ and D. Truhn and J. N. Kather},
      title        = {{S}warm learning for decentralized artificial intelligence
                      in cancer histopathology.},
      journal      = {Nature medicine},
      volume       = {28},
      number       = {6},
      issn         = {1078-8956},
      address      = {New York, NY},
      publisher    = {Nature America Inc.},
      reportid     = {DKFZ-2022-00845},
      pages        = {1232-1239},
      year         = {2022},
      note         = {2022 Jun;28(6):1232-1239},
      abstract     = {Artificial intelligence (AI) can predict the presence of
                      molecular alterations directly from routine histopathology
                      slides. However, training robust AI systems requires large
                      datasets for which data collection faces practical, ethical
                      and legal obstacles. These obstacles could be overcome with
                      swarm learning (SL), in which partners jointly train AI
                      models while avoiding data transfer and monopolistic data
                      governance. Here, we demonstrate the successful use of SL in
                      large, multicentric datasets of gigapixel histopathology
                      images from over 5,000 patients. We show that AI models
                      trained using SL can predict BRAF mutational status and
                      microsatellite instability directly from hematoxylin and
                      eosin $(H\&E)-stained$ pathology slides of colorectal
                      cancer. We trained AI models on three patient cohorts from
                      Northern Ireland, Germany and the United States, and
                      validated the prediction performance in two independent
                      datasets from the United Kingdom. Our data show that
                      SL-trained AI models outperform most locally trained models,
                      and perform on par with models that are trained on the
                      merged datasets. In addition, we show that SL-based AI
                      models are data efficient. In the future, SL can be used to
                      train distributed AI models for any histopathology image
                      analysis task, eliminating the need for data transfer.},
      cin          = {C070 / C120 / HD01 / C140 / C020},
      ddc          = {610},
      cid          = {I:(DE-He78)C070-20160331 / I:(DE-He78)C120-20160331 /
                      I:(DE-He78)HD01-20160331 / I:(DE-He78)C140-20160331 /
                      I:(DE-He78)C020-20160331},
      pnm          = {313 - Krebsrisikofaktoren und Prävention (POF4-313)},
      pid          = {G:(DE-HGF)POF4-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:35469069},
      doi          = {10.1038/s41591-022-01768-5},
      url          = {https://inrepo02.dkfz.de/record/179696},
}