58 citations · 204 across the 20 of their papers we have counts for
7 papers · 1 filter
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…
Suggestive Annotation of Brain Tumour Images with Gradient-guided Sampling
Chengliang Dai, Shuo Wang, Yuanhan Mo +4
Machine learning has been widely adopted for medical image analysis in recent years given its promising performance in image segmentation and classification tasks. As a data-driven…
Deep Generative Model-based Quality Control for Cardiac MRI Segmentation
Shuo Wang, Giacomo Tarroni, Chen Qin +7
In recent years, convolutional neural networks have demonstrated promising performance in a variety of medical image segmentation tasks. However, when a trained segmentation model…
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…
A Bayesian Updating Scheme for Pandemics: Estimating the Infection Dynamics of COVID-19
Shuo Wang, Xian Yang, Ling Li +5
Epidemic models play a key role in understanding and responding to the emerging COVID-19 pandemic. Widely used compartmental models are static and are of limited use to evaluate in…
Efficient Deep Representation Learning by Adaptive Latent Space Sampling
Yuanhan Mo, Shuo Wang, Chengliang Dai +4
Supervised deep learning requires a large amount of training samples with annotations (e.g. label class for classification task, pixel- or voxel-wised label map for segmentation ta…
An Epidemiological Modelling Approach for Covid19 via Data Assimilation
Philip Nadler, Shuo Wang, Rossella Arcucci +2
The global pandemic of the 2019-nCov requires the evaluation of policy interventions to mitigate future social and economic costs of quarantine measures worldwide. We propose an ep…