19 citations · 35 across the 4 of their papers we have counts for
4 papers
Towards Trustworthy AI for Glioma Diagnosis: A Task-Aware Evaluation of Uncertainty Quantification
Gonzalo Esteban Mosquera Rojas, Sebastian R. van der Voort, Carolin M. Pirkl +3
Uncertainty Quantification (UQ) is a key requirement for trustworthy AI in high-stakes medical image analysis. In this work, we evaluate UQ in a multi-task Deep Learning framework…
Region of Interest focused MRI to Synthetic CT Translation using Regression and Classification Multi-task Network
Sandeep Kaushik, Mikael Bylund, Cristina Cozzini +12
In this work, we present a method for synthetic CT (sCT) generation from zero-echo-time (ZTE) MRI aimed at structural and quantitative accuracies of the image, with a particular fo…
Unsupervised-learning-based method for chest MRI-CT transformation using structure constrained unsupervised generative attention networks
Hidetoshi Matsuo, Mizuho Nishio, Munenobu Nogami +6
The integrated positron emission tomography/magnetic resonance imaging (PET/MRI) scanner facilitates the simultaneous acquisition of metabolic information via PET and morphological…
Attenuation Coefficient Estimation for PET/MRI With Bayesian Deep Learning pseudo-CT and Maximum Likelihood Estimation of Activity and Attenuation
Andrew P. Leynes, Sangtae P. Ahn, Kristen A. Wangerin +4
A major remaining challenge for magnetic resonance-based attenuation correction methods (MRAC) is their susceptibility to sources of MRI artifacts (e.g. implants, motion) and uncer…