IEEE

Uncertainty Quantification and Calibration in Full-Wave Inverse Scattering Problems With Evidential Neural Networks

Abstract: Recently, deep learning-based solvers for inverse scattering problems (ISPs) have been continuously developed. The ill-posedness and nonlinear nature of ISPs make deep learning-based ISP ...
The Lancet

Effective sample size for individual risk predictions: quantifying uncertainty in machine learning models

Individual prediction uncertainty is a key aspect of clinical prediction model performance; however, standard performance ...
IEEE

Linear Assumptions Based RF Pulse Calibration Framework Using the Bloch-Siegert Shift

Abstract: Accurately calibrating the radiofrequency (RF) transmission chain is critical for ensuring the reliability of magnetic resonance imaging (MRI) scans. The complexities of MRI, including coil ...
GitHub

wgcban/mix-bt

$C^{MA}_{gt} = \lambda (Z^A)^TZ^A + (1-\lambda)\mathtt{Shuffle}^*(Z^B)^TZ^A$ $C^{MB}_{gt} = \lambda (Z^A)^TZ^B + (1-\lambda)\mathtt{Shuffle}^*(Z^B)^TZ^B$ ...