35 citations · 37 across the 7 of their papers we have counts for
10 papers
Improving the Scan-rescan Precision of AI-based CMR Biomarker Estimation
Dewmini Hasara Wickremasinghe, Yiyang Xu, Esther Puyol-Antón +3
Quantification of cardiac biomarkers from cine cardiovascular magnetic resonance (CMR) data using deep learning (DL) methods offers many advantages, such as increased accuracy and…
Improved 3D Whole Heart Geometry from Sparse CMR Slices
Yiyang Xu, Hao Xu, Matthew Sinclair +6
Cardiac magnetic resonance (CMR) imaging and computed tomography (CT) are two common non-invasive imaging methods for assessing patients with cardiovascular disease. CMR typically…
An investigation into the causes of race bias in AI-based cine CMR segmentation
Tiarna Lee, Esther Puyol-Anton, Bram Ruijsink +5
Artificial intelligence (AI) methods are being used increasingly for the automated segmentation of cine cardiac magnetic resonance (CMR) imaging. However, these methods have been s…
Uncertainty Aware Training to Improve Deep Learning Model Calibration for Classification of Cardiac MR Images
Tareen Dawood, Chen Chen, Baldeep S. Sidhua +9
Quantifying uncertainty of predictions has been identified as one way to develop more trustworthy artificial intelligence (AI) models beyond conventional reporting of performance m…
Bias in Unsupervised Anomaly Detection in Brain MRI
Cosmin I. Bercea, Esther Puyol-Antón, Benedikt Wiestler +3
Unsupervised anomaly detection methods offer a promising and flexible alternative to supervised approaches, holding the potential to revolutionize medical scan analysis and enhance…
An investigation into the impact of deep learning model choice on sex and race bias in cardiac MR segmentation
Tiarna Lee, Esther Puyol-Antón, Bram Ruijsink +3
In medical imaging, artificial intelligence (AI) is increasingly being used to automate routine tasks. However, these algorithms can exhibit and exacerbate biases which lead to dis…