Matteo Gätzner

Provably Valid Uncertainty Quantification for Deep Computed Tomography

We present a principled framework for uncertainty quantification in computed tomography (CT) reconstruction. Based on the sequential likelihood mixing framework (Kirschner et al., 2025), we establish the first confidence regions with theoretical coverage guarantees for deep learning-based CT reconst…