4 papers
Comparison of Loss Functions for Robust Deep Learning-based Echocardiography Segmentation when Learning with Partially Labelled Data from Multiple Domains
Iman Islam, Esther Puyol-Antón, Bram Ruijsink +2
Echocardiography is the first imaging modality used for assessing cardiac function, and accurate segmentation of cardiac structures is essential for deriving biomarkers. However, t…
Detecting and refurbishing ground truth errors during training of deep learning-based echocardiography segmentation models
Iman Islam, Bram Ruijsink, Andrew J. Reader +1
Deep learning-based medical image segmentation typically relies on ground truth (GT) labels obtained through manual annotation, but these can be prone to random errors or systemati…
Understanding-informed Bias Mitigation for Fair CMR Segmentation
Tiarna Lee, Esther Puyol-Antón, Bram Ruijsink +6
Artificial intelligence (AI) is increasingly being used for medical imaging tasks. However, there can be biases in AI models, particularly when they are trained using imbalanced tr…
Cardiac Digital Twins at Scale from MRI: Open Tools and Representative Models from ~55000 UK Biobank Participants
Devran Ugurlu, Shuang Qian, Elliot Fairweather +10
A cardiac digital twin is a virtual replica of a patient's heart for screening, diagnosis, prognosis, risk assessment, and treatment planning of cardiovascular diseases. This requi…