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@ARTICLE{Fonck:1035328,
      author       = {Fonck, Simon and Fritsch, Sebastian and Nguyen, Alina and
                      Kowalewski, Stefan and Stollenwerk, André},
      title        = {{R}obustness of a {D}ense{N}et-121 for the {C}lassification
                      of {ARDS} in {C}hest {X}-{R}ays},
      journal      = {Current directions in biomedical engineering},
      volume       = {10},
      number       = {4},
      issn         = {2364-5504},
      address      = {Berlin},
      publisher    = {De Gruyter},
      reportid     = {FZJ-2025-00382},
      pages        = {244 - 247},
      year         = {2024},
      abstract     = {Research in the field of artificial intelligence (AI) in
                      medicine is increasingly relying on algorithms based on deep
                      learning (DL), especially for radiology. Despite producing
                      promising results, DL models have a major drawback: their
                      reliance on large training datasets. Especially in medicine,
                      large, annotated datasets are hard to obtain, leading to low
                      robustness and a performance loss when exposed to unseen,
                      new data. To address this problem, our research evaluates
                      how well data augmentation is able to expand the used
                      dataset and thus improve a DL model. We employ 17 different
                      augmentation methods to test the robustness of a
                      DenseNet-121 trained to classify Acute Respiratory Distress
                      Syndrome (ARDS) in chest X-rays. Our experiments show that
                      while the model has low robustness for augmented test data
                      when trained on unaugmented data, the general performance
                      for ARDS classification can be improved by augmenting the
                      training data. Overall, this demonstrates that data
                      augmentation is beneficial in training AI models for ARDS
                      classification in order to create more robust and
                      generalizable models.},
      cin          = {JSC / CASA},
      ddc          = {570},
      cid          = {I:(DE-Juel1)JSC-20090406 / I:(DE-Juel1)CASA-20230315},
      pnm          = {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)POF4-5112 / G:(BMBF)01IS22095D},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1515/cdbme-2024-2059},
      url          = {https://juser.fz-juelich.de/record/1035328},
}