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@ARTICLE{Eulig:293809,
      author       = {E. Eulig$^*$ and F. Jäger$^*$ and J. Maier$^*$ and B.
                      Ommer and M. Kachelriess$^*$},
      title        = {{R}econstructing and analyzing the invariances of low-dose
                      {CT} image denoising networks.},
      journal      = {Medical physics},
      volume       = {52},
      number       = {1},
      issn         = {0094-2405},
      address      = {College Park, Md.},
      publisher    = {AAPM},
      reportid     = {DKFZ-2024-01967},
      pages        = {188-200},
      year         = {2025},
      note         = {#EA:E025#LA:E025# / 2025 Jan;52(1):188-200},
      abstract     = {Deep learning-based methods led to significant advancements
                      in many areas of medical imaging, most of which are
                      concerned with the reduction of artifacts caused by motion,
                      scatter, or noise. However, with most neural networks being
                      black boxes, they remain notoriously difficult to interpret,
                      hindering their clinical implementation. In particular, it
                      has been shown that networks exhibit invariances w.r.t.
                      input features, that is, they learn to ignore certain
                      information in the input data.To improve the
                      interpretability of deep learning-based low-dose CT image
                      denoising networks.We learn a complete data representation
                      of low-dose input images using a conditional variational
                      autoencoder (cVAE). In this representation, invariances of
                      any given denoising network are then disentangled from the
                      information it is not invariant to using a conditional
                      invertible neural network (cINN). At test time, image-space
                      invariances are generated by applying the inverse of the
                      cINN and subsequent decoding using the cVAE. We propose two
                      methods to analyze sampled invariances and to find those
                      that correspond to alterations of anatomical structures.The
                      proposed method is applied to four popular deep
                      learning-based low-dose CT image denoising networks. We find
                      that the networks are not only invariant to noise amplitude
                      and realizations, but also to anatomical structures.The
                      proposed method is capable of reconstructing and analyzing
                      invariances of deep learning-based low-dose CT image
                      denoising networks. This is an important step toward
                      interpreting deep learning-based methods for medical
                      imaging, which is essential for their clinical
                      implementation.},
      keywords     = {computed tomography (Other) / deep learning (Other) /
                      explainability (Other) / invariances (Other) / low‐dose
                      (Other) / robustness (Other)},
      cin          = {E025},
      ddc          = {610},
      cid          = {I:(DE-He78)E025-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:39348044},
      doi          = {10.1002/mp.17413},
      url          = {https://inrepo02.dkfz.de/record/293809},
}