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@ARTICLE{Fischer:305635,
      author       = {M. Fischer$^*$ and A. Muckenhuber and R. Peretzke$^*$ and
                      L. Farah$^*$ and C. Ulrich Harsy$^*$ and S. Ziegler$^*$ and
                      P. Schader$^*$ and L. Feineis$^*$ and H. Gao$^*$ and S.
                      Xiao$^*$ and M. Götz$^*$ and M. Nolden$^*$ and K.
                      Steiger$^*$ and J. T. Sieveke$^*$ and L. Endrös and R.
                      Braren$^*$ and J. Kleesiek$^*$ and P. Schüffler and P.
                      Neher$^*$ and K. Maier-Hein$^*$},
      title        = {{C}ontrastive virtual staining enhances deep learning-based
                      {PDAC} subtyping from ${H}\&{E}-stained$ tissue cores.},
      journal      = {The journal of pathology},
      volume       = {nn},
      issn         = {0368-3494},
      address      = {Bognor Regis [u.a.]},
      publisher    = {Wiley},
      reportid     = {DKFZ-2025-02277},
      pages        = {nn},
      year         = {2025},
      note         = {#EA:E230#LA:E230# / epub},
      abstract     = {Pancreatic ductal adenocarcinoma (PDAC) subtyping typically
                      relies on immunohistochemistry (IHC) staining for critical
                      markers like HNF1A and KRT81, a labor-intensive manual
                      staining process that introduces variability. Virtual
                      staining methods offer promising alternatives by generating
                      synthetic IHC images from routine hematoxylin and eosin
                      $(H\&E)$ slides. However, most current approaches evaluate
                      success by image quality measures rather than assessing
                      diagnostically relevant features. Here, we introduce a novel
                      cycleGAN framework utilizing a contrastive-inspired approach
                      trained on semipaired datasets derived from consecutive
                      tissue sections. Our method significantly enhances PDAC
                      subtyping accuracy based on synthetic IHC images generated
                      from standard $H\&E$ inputs, improving the classification
                      F1-score from 0.66 to 0.77 for KRT81 and from 0.61 to 0.73
                      for HNF1A, compared with classification directly on $H\&E$
                      images. This approach also substantially outperforms
                      baseline CycleGAN models. These results underscore the
                      clinical potential of contrastive virtual staining to
                      streamline PDAC diagnostics and improve their robustness. ©
                      2025 The Author(s). The Journal of Pathology published by
                      John Wiley $\&$ Sons Ltd on behalf of The Pathological
                      Society of Great Britain and Ireland.},
      keywords     = {CycleGAN (Other) / $H\&E$ (Other) / IHC (Other) / PDAC
                      subtyping (Other) / contrastive learning (Other) / digital
                      pathology (Other) / virtual staining (Other)},
      cin          = {E230 / ED01 / MU01},
      ddc          = {610},
      cid          = {I:(DE-He78)E230-20160331 / I:(DE-He78)ED01-20160331 /
                      I:(DE-He78)MU01-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:41188199},
      doi          = {10.1002/path.6491},
      url          = {https://inrepo02.dkfz.de/record/305635},
}