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@INPROCEEDINGS{Conjeti:145640,
      author       = {Conjeti, Sailesh},
      title        = {{G}eneralizability vs. {R}obustness: {I}nvestigating
                      {M}edical {I}maging {N}etworks {U}sing {A}dversarial
                      {E}xamples},
      reportid     = {DZNE-2020-00970},
      year         = {2018},
      abstract     = {In this paper, for the first time, we propose an evaluation
                      method for deep learning models that assesses the
                      performance of a model not only in an unseen test scenario,
                      but also in extreme cases of noise, outliers and ambiguous
                      input data. To this end, we utilize adversarial examples,
                      images that fool machine learning models, while looking
                      imperceptibly different from original data, as a measure to
                      evaluate the robustness of a variety of medical imaging
                      models. Through extensive experiments on skin lesion
                      classification and whole brain segmentation with
                      state-of-the-art networks such as Inception and UNet, we
                      show that models that achieve comparable performance
                      regarding generalizability may have significant variations
                      in their perception of the underlying data manifold, leading
                      to an extensive performance gap in their robustness.},
      month         = {Sep},
      date          = {2018-09-16},
      organization  = {MICCAI 2018, Granada (Spain), 16 Sep
                       2018 - 16 Sep 2018},
      subtyp        = {Other},
      cin          = {AG Reuter},
      cid          = {I:(DE-2719)1040310},
      pnm          = {345 - Population Studies and Genetics (POF3-345)},
      pid          = {G:(DE-HGF)POF3-345},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://pub.dzne.de/record/145640},
}