[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.
