activity
20192021
most citedRealistic Adversarial Data Augmentation for MR Image Segmentation

10 citations · 26 across the 7 of their papers we have counts for

collaborators

10 papers

cs.MM2021

Product semantics translation from brain activity via adversarial learning

Pan Wang, Zhifeng Gong, Shuo Wang +5

A small change of design semantics may affect a user's satisfaction with a product. To modify a design semantic of a given product from personalised brain activity via adversarial…

q-bio.NC20214 cited

A General Framework for Revealing Human Mind with auto-encoding GANs

Pan Wang, Rui Zhou, Shuo Wang +6

Addressing the question of visualising human mind could help us to find regions that are associated with observed cognition and responsible for expressing the elusive mental image,…

eess.IV20201 cited

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…

cs.CV20205 cited

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…

eess.IV2020

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…

eess.IV202010 cited

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…