26 citations · 30 across the 6 of their papers we have counts for
6 papers
Evaluation of the Synthetic Electronic Health Records
Emily Muller, Xu Zheng, Jer Hayes
Generative models have been found effective for data synthesis due to their ability to capture complex underlying data distributions. The quality of generated data from these model…
Synthesising Electronic Health Records: Cystic Fibrosis Patient Group
Emily Muller, Xu Zheng, Jer Hayes
Class imbalance can often degrade predictive performance of supervised learning algorithms. Balanced classes can be obtained by oversampling exact copies, with noise, or interpolat…
Uncertainty-Aware Deep Co-training for Semi-supervised Medical Image Segmentation
Xu Zheng, Chong Fu, Haoyu Xie +3
Semi-supervised learning has made significant strides in the medical domain since it alleviates the heavy burden of collecting abundant pixel-wise annotated data for semantic segme…
Network Generation with Differential Privacy
Xu Zheng, Nicholas McCarthy, Jer Hayes
We consider the problem of generating private synthetic versions of real-world graphs containing private information while maintaining the utility of generated graphs. Differential…
A Graph-based Imputation Method for Sparse Medical Records
Ramon Vinas, Xu Zheng, Jer Hayes
Electronic Medical Records (EHR) are extremely sparse. Only a small proportion of events (symptoms, diagnoses, and treatments) are observed in the lifetime of an individual. The hi…
STaDA: Style Transfer as Data Augmentation
Xu Zheng, Tejo Chalasani, Koustav Ghosal +2
The success of training deep Convolutional Neural Networks (CNNs) heavily depends on a significant amount of labelled data. Recent research has found that neural style transfer alg…