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@ARTICLE{Adler:298417,
      author       = {T. Adler$^*$ and J.-H. Nölke$^*$ and A. Reinke$^*$ and M.
                      D. Tizabi$^*$ and S. Gruber$^*$ and D. Trofimova$^*$ and L.
                      Ardizzone and P. F. Jaeger$^*$ and F. Buettner$^*$ and U.
                      Köthe and L. Maier-Hein$^*$},
      title        = {{A}pplication-driven validation of posteriors in inverse
                      problems.},
      journal      = {Medical image analysis},
      volume       = {101},
      issn         = {1361-8415},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {DKFZ-2025-00273},
      pages        = {103474},
      year         = {2025},
      note         = {#EA:E130#LA:E130#},
      abstract     = {Current deep learning-based solutions for image analysis
                      tasks are commonly incapable of handling problems to which
                      multiple different plausible solutions exist. In response,
                      posterior-based methods such as conditional Diffusion Models
                      and Invertible Neural Networks have emerged; however, their
                      translation is hampered by a lack of research on adequate
                      validation. In other words, the way progress is measured
                      often does not reflect the needs of the driving practical
                      application. Closing this gap in the literature, we present
                      the first systematic framework for the application-driven
                      validation of posterior-based methods in inverse problems.
                      As a methodological novelty, it adopts key principles from
                      the field of object detection validation, which has a long
                      history of addressing the question of how to locate and
                      match multiple object instances in an image. Treating modes
                      as instances enables us to perform mode-centric validation,
                      using well-interpretable metrics from the application
                      perspective. We demonstrate the value of our framework
                      through instantiations for a synthetic toy example and two
                      medical vision use cases: pose estimation in surgery and
                      imaging-based quantification of functional tissue parameters
                      for diagnostics. Our framework offers key advantages over
                      common approaches to posterior validation in all three
                      examples and could thus revolutionize performance assessment
                      in inverse problems.},
      keywords     = {Deep learning (Other) / Inverse problems (Other) / Metrics
                      (Other) / Posterior (Other) / Validation (Other)},
      cin          = {E130 / FM01 / E290},
      ddc          = {610},
      cid          = {I:(DE-He78)E130-20160331 / I:(DE-He78)FM01-20160331 /
                      I:(DE-He78)E290-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:39892221},
      doi          = {10.1016/j.media.2025.103474},
      url          = {https://inrepo02.dkfz.de/record/298417},
}