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@INPROCEEDINGS{Busch:1046032,
      author       = {Busch, J. S. and Barakat, C. S. and Pauli, T. and Fonck, S.
                      and Stollenwerk, A. and Fritsch, S. J. and Riedel, Morris},
      title        = {{L}everaging {V}ision {T}ransformers with {H}yperparameter
                      {O}ptimization for the {C}lassification of {A}cute
                      {R}espiratory {D}istress {S}yndrome},
      publisher    = {IEEE},
      reportid     = {FZJ-2025-03672},
      pages        = {1293-1298},
      year         = {2025},
      abstract     = {Acute respiratory distress syndrome (ARDS) is a serious
                      lung condition associated with a high mortality rate. The
                      classification of this condition poses a challenge in
                      intensive care medicine and diagnostic imaging. Artificial
                      intelligence methods, particularly deep learning, can assist
                      the diagnostic process. Recent advances have demonstrated
                      the potential of vision transformers to improve image
                      analysis through their ability to extract relevant features
                      and complex patterns in images. In this study, a vision
                      transformer is implemented for classifying ARDS in chest
                      X-Rays using a two-step transfer learning approach. For this
                      purpose, publicly available databases of X-rays are used,
                      some of which have been annotated by a radiologist for the
                      use case ARDS. Furthermore, in order to uncover the optimal
                      combination of model parameters to streamline the training
                      process, we implement a two-tier hyperparameter optimization
                      using the Ray Tune framework on high-performance computing
                      infrastructure. The retrained vision transformer was able to
                      classify ARDS data with $95\%$ accuracy, outperforming
                      previous approaches employing residual networks. Our results
                      highlight the improvement that can be achieved through a
                      two-step transfer learning approach leveraging vision
                      transformers and taking advantage of powerful supercomputing
                      architecture. Ultimately, our work facilitates timely and
                      accurate classification of ARDS thereby enabling improved
                      outcomes for patients receiving critical care.},
      month         = {Jun},
      date          = {2025-06-02},
      organization  = {2025 MIPRO 48th ICT and Electronics
                       Convention, Opatija (Croatia), 2 Jun
                       2025 - 6 Jun 2025},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / 5112 - Cross-Domain
                      Algorithms, Tools, Methods Labs (ATMLs) and Research Groups
                      (POF4-511) / SDI-S - SDI-S: Smart Data Innovation Services -
                      Experimentelle Erprobung und Entwicklung von
                      KI-Dienstverbünden für Innovationen auf industriellen
                      Daten (01IS22095D)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(DE-HGF)POF4-5112 /
                      G:(BMBF)01IS22095D},
      typ          = {PUB:(DE-HGF)8},
      doi          = {10.1109/MIPRO65660.2025.11131920},
      url          = {https://juser.fz-juelich.de/record/1046032},
}