h1

% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Hlscher:946026,
      author       = {Hölscher, David Laurin and Bouteldja, Nassim and Joodaki,
                      Mehdi and Russo, Maria L. and Lan, Yu-Chia and Sadr, Alireza
                      Vafaei and Cheng, Mingbo and Tesar, Vladimir and von
                      Stillfried, Saskia and Klinkhammer, Barbara Mara and
                      Barratt, Jonathan and Flöge, Jürgen and Roberts, Ian S. D.
                      and Coppo, Rosanna and Costa, Ivan G. and Bülow, Roman
                      David and Boor, Peter},
      title        = {{N}ext-{G}eneration {M}orphometry for pathomics-data mining
                      in histopathology},
      journal      = {Nature Communications},
      volume       = {14},
      issn         = {2041-1723},
      address      = {[London]},
      publisher    = {Nature Publishing Group UK},
      reportid     = {RWTH-2023-01537},
      pages        = {470},
      year         = {2023},
      cin          = {528001-2 / 530000-5 / 531020-2},
      ddc          = {500},
      cid          = {$I:(DE-82)528001-2_20140620$ / $I:(DE-82)530000-5_20190109$
                      / $I:(DE-82)531020-2_20140620$},
      pnm          = {OA - Open Access Publikation mit Unterstützung der
                      Universitätsbibliothek der RWTH Aachen University
                      (X021000-OA) / 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) /
                      BMG-ZMVI1-2520DAT111 - Diagnosestellung und
                      Risikostratifizierung von Lebererkrankungen mittels Deep
                      Learning anhand von klinischen Routinedaten (DEEP LIVER)
                      (BMG-ZMVI1-2520DAT111) / BMWK 01MK2002A - 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:(GEPRIS)322900939 /
                      G:(GEPRIS)454024652 / G:(GEPRIS)432698239 /
                      G:(GEPRIS)445703531 / G:(EU-Grant)101001791 /
                      G:(BMBF)01GM1901A / G:(DE-82)BMG-ZMVI1-2520DAT111 /
                      G:(BMWK)01MK2002A},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000955886200006},
      pubmed       = {pmid:36709324},
      doi          = {10.1038/s41467-023-36173-0},
      url          = {https://publications.rwth-aachen.de/record/946026},
}