10 citations · 19 across the 4 of their papers we have counts for
7 papers
Uncertainty quantification in non-rigid image registration via stochastic gradient Markov chain Monte Carlo
Daniel Grzech, Mohammad Farid Azampour, Huaqi Qiu +3
We develop a new Bayesian model for non-rigid registration of three-dimensional medical images, with a focus on uncertainty quantification. Probabilistic registration of large imag…
Is MC Dropout Bayesian?
Loic Le Folgoc, Vasileios Baltatzis, Sujal Desai +7
MC Dropout is a mainstream "free lunch" method in medical imaging for approximate Bayesian computations (ABC). Its appeal is to solve out-of-the-box the daunting task of ABC and un…
Biomechanics-informed Neural Networks for Myocardial Motion Tracking in MRI
Chen Qin, Shuo Wang, Chen Chen +3
Image registration is an ill-posed inverse problem which often requires regularisation on the solution space. In contrast to most of the current approaches which impose explicit re…
Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotation
Cheng Ouyang, Carlo Biffi, Chen Chen +3
Few-shot semantic segmentation (FSS) has great potential for medical imaging applications. Most of the existing FSS techniques require abundant annotated semantic classes for train…
Realistic Adversarial Data Augmentation for MR Image Segmentation
Chen Chen, Chen Qin, Huaqi Qiu +6
Neural network-based approaches can achieve high accuracy in various medical image segmentation tasks. However, they generally require large labelled datasets for supervised learni…
Deep learning for cardiac image segmentation: A review
Chen Chen, Chen Qin, Huaqi Qiu +4
Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation pap…