3 accepted papers
Over recent years, deep learning based image registration has achieved impressive accuracy in many domains, including medical imaging and, specifically, human neuroimaging with magnetic resonance imaging (MRI). However, the uncertainty estimation associated with these methods has been largely limite…
Domain randomization through synthesis is a powerful strategy to train networks that are unbiased with respect to the domain of the input images. Randomization allows networks to see a virtually infinite range of intensities and artifacts during training, thereby minimizing overfitting to appearance…
"Recent learning-based approaches have made astonishing advances in calibrated medical imaging like computerized tomography (CT). Yet, they struggle to generalize in uncalibrated modalities – notably magnetic resonance (MR) imaging, where performance is highly sensitive to the differences in MR cont…