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@ARTICLE{Barakat:943307,
      author       = {Barakat, Chadi and Aach, Marcel and Schuppert, Andreas and
                      Brynjólfsson, Sigurður and Fritsch, Sebastian and Riedel,
                      Morris},
      title        = {{A}nalysis of {C}hest {X}-ray for {COVID}-19 {D}iagnosis as
                      a {U}se {C}ase for an {HPC}-{E}nabled {D}ata {A}nalysis and
                      {M}achine {L}earning {P}latform for {M}edical {D}iagnosis
                      {S}upport},
      journal      = {Diagnostics},
      volume       = {13},
      number       = {3},
      issn         = {2075-4418},
      address      = {Basel},
      publisher    = {MDPI},
      reportid     = {FZJ-2023-00913},
      pages        = {391},
      year         = {2023},
      abstract     = {The COVID-19 pandemic shed light on the need for quick
                      diagnosis tools in healthcare, leading to the development of
                      several algorithmic models for disease detection. Though
                      these models are relatively easy to build, their training
                      requires a lot of data, storage, and resources, which may
                      not be available for use by medical institutions or could be
                      beyond the skillset of the people who most need these tools.
                      This paper describes a data analysis and machine learning
                      platform that takes advantage of high-performance computing
                      infrastructure for medical diagnosis support applications.
                      This platform is validated by re-training a previously
                      published deep learning model (COVID-Net) on new data, where
                      it is shown that the performance of the model is improved
                      through large-scale hyperparameter optimisation that
                      uncovered optimal training parameter combinations. The
                      per-class accuracy of the model, especially for COVID-19 and
                      pneumonia, is higher when using the tuned hyperparameters
                      (healthy: $96.5\%;$ pneumonia: $61.5\%;$ COVID-19: $78.9\%)$
                      as opposed to parameters chosen through traditional methods
                      (healthy: $93.6\%;$ pneumonia: $46.1\%;$ COVID-19:
                      $76.3\%).$ Furthermore, training speed-up analysis shows a
                      major decrease in training time as resources increase, from
                      207 min using 1 node to 54 min when distributed over 32
                      nodes, but highlights the presence of a cut-off point where
                      the communication overhead begins to affect performance. The
                      developed platform is intended to provide the medical field
                      with a technical environment for developing novel portable
                      artificial-intelligence-based tools for diagnosis support.},
      cin          = {JSC},
      ddc          = {610},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511) / RAISE - Research on AI- and
                      Simulation-Based Engineering at Exascale (951733) / EUROCC -
                      National Competence Centres in the framework of EuroHPC
                      (951732) / SMITH - Medizininformatik-Konsortium - Beitrag
                      Forschungszentrum Jülich (01ZZ1803M)},
      pid          = {G:(DE-HGF)POF4-5112 / G:(EU-Grant)951733 /
                      G:(EU-Grant)951732 / G:(BMBF)01ZZ1803M},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {36766496},
      UT           = {WOS:000933780900001},
      doi          = {10.3390/diagnostics13030391},
      url          = {https://juser.fz-juelich.de/record/943307},
}