3 citations · 4 across the 6 of their papers we have counts for
5 papers · 1 filter
Temporal Graph Offset Reconstruction: Towards Temporally Robust Graph Representation Learning
Stephen Bonner, John Brennan, Ibad Kureshi +3
Graphs are a commonly used construct for representing relationships between elements in complex high dimensional datasets. Many real-world phenomenon are dynamic in nature, meaning…
Curvilinear Structure Enhancement by Multiscale Top-Hat Tensor in 2D/3D Images
Shuaa S. Alharbi, Cigdem Sazak, Carl J. Nelson +1
A wide range of biomedical applications requires enhancement, detection, quantification and modelling of curvilinear structures in 2D and 3D images. Curvilinear structure enhanceme…
Style Augmentation: Data Augmentation via Style Randomization
Philip T. Jackson, Amir Atapour-Abarghouei, Stephen Bonner +2
We introduce style augmentation, a new form of data augmentation based on random style transfer, for improving the robustness of convolutional neural networks (CNN) over both class…
Exploring the Semantic Content of Unsupervised Graph Embeddings: An Empirical Study
Stephen Bonner, Ibad Kureshi, John Brennan +3
Graph embeddings have become a key and widely used technique within the field of graph mining, proving to be successful across a broad range of domains including social, citation,…
The Multiscale Bowler-Hat Transform for Vessel Enhancement in 3D Biomedical Images
Cigdem Sazak, Carl J. Nelson, Boguslaw Obara
Enhancement and detection of 3D vessel-like structures has long been an open problem as most existing image processing methods fail in many aspects, including a lack of uniform enh…