h1

% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@PHDTHESIS{Han:977930,
      author       = {Han, Tianyu},
      othercontributors = {Schulz, Volkmar Adolf and Stahl, Achim},
      title        = {{G}enerative modeling for medical image analysis},
      school       = {RWTH Aachen University},
      type         = {Dissertation},
      address      = {Aachen},
      publisher    = {RWTH Aachen University},
      reportid     = {RWTH-2024-00999},
      pages        = {1 Online-Ressource : Illustrationen},
      year         = {2024},
      note         = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
                      University; Dissertation, RWTH Aachen University, 2024},
      abstract     = {Radiology is undergoing a transformation with the
                      increasing integration of machine learning into medical
                      imaging. This promises enhanced diagnostic precision and
                      faster diagnosis. Yet, creating potent machine learning
                      algorithms for this domain often demands vast volumes of
                      labeled data. In intricate radiological scenarios,
                      annotating these images can be both time-intensive and
                      costly. To counteract these hurdles, we advocate for
                      unsupervised learning methodologies in medical imaging
                      analysis. We place emphasis on the deployment of a
                      self-supervised generative model. This model is tailored to
                      decipher latent temporal trajectories from longitudinal
                      radiographs of patients, thereby facilitating model-driven
                      forecasts regarding the emergence and evolution of
                      osteoarthritis. Further, we introduce an innovative paradigm
                      aimed at publicly sharing medical imaging data for algorithm
                      training. This ensures the safeguarding of sensitive patient
                      information. Alongside, we underline the imperative to
                      elevate the transparency of deep learning in a clinical
                      setting and bolster the algorithms’ resilience to
                      adversarial onslaughts. In essence, by capitalizing on
                      unsupervised learning strategies and pioneering ways to make
                      medical imaging data both accessible and secure, we stand on
                      the cusp of redefining radiological research, all while
                      elevating patient care outcomes.},
      cin          = {133510 / 130000},
      ddc          = {530},
      cid          = {$I:(DE-82)133510_20140620$ / $I:(DE-82)130000_20140620$},
      typ          = {PUB:(DE-HGF)11},
      doi          = {10.18154/RWTH-2024-00999},
      url          = {https://publications.rwth-aachen.de/record/977930},
}