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[02164] Uncertainty-Aware Null Space Networks for Data-Consistent Image Reconstruction

  • Session Time & Room : 3D (Aug.23, 15:30-17:10) @E811
  • Type : Contributed Talk
  • Abstract : State-of-the-art reconstruction methods in inverse problems have been developed by incorporating latest advances in deep learning. Before learning approaches can be used in safety-critical areas like medical imaging, a model must not only provide a reconstruction, but also an estimate of its reliability. This study presents a cascaded architecture of null space networks and combines it with recent progress of uncertainty quantification in computer vision. This way, two key properties are met: data-consistency and uncertainty-awareness.
  • Classification : 68T07, 68T37, 92C50, 92C55
  • Format : Talk at Waseda University
  • Author(s) :
    • Christoph Angermann (VASCage – Research Centre on Vascular Ageing and Stroke)
    • Simon Goeppel (Universität Innsbruck)
    • Markus Haltmeier (Universität Innsbruck)