35 citations · 41 across the 12 of their papers we have counts for
12 papers
Multi-task learning for joint weakly-supervised segmentation and aortic arch anomaly classification in fetal cardiac MRI
Paula Ramirez, Alena Uus, Milou P. M. van Poppel +6
Congenital Heart Disease (CHD) is a group of cardiac malformations present already during fetal life, representing the prevailing category of birth defects globally. Our aim in thi…
Comparison of deep-learning data fusion strategies in mandibular osteoradionecrosis prediction modelling using clinical variables and radiation dose distribution volumes
Laia Humbert-Vidan, Vinod Patel, Andrew P King +1
Purpose. NTCP modelling is rapidly embracing DL methods as the need to include spatial dose information is acknowledged. Finding the most appropriate way of combining radiation dos…
An Investigation Into Race Bias in Random Forest Models Based on Breast DCE-MRI Derived Radiomics Features
Mohamed Huti, Tiarna Lee, Elinor Sawyer +1
Recent research has shown that artificial intelligence (AI) models can exhibit bias in performance when trained using data that are imbalanced by protected attribute(s). Most work…
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…