35 citations · 65 across the 5 of their papers we have counts for
5 papers
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…
AI-enabled Assessment of Cardiac Systolic and Diastolic Function from Echocardiography
Esther Puyol-Antón, Bram Ruijsink, Baldeep S. Sidhu +12
Left ventricular (LV) function is an important factor in terms of patient management, outcome, and long-term survival of patients with heart disease. The most recently published cl…
Uncertainty-Aware Training for Cardiac Resynchronisation Therapy Response Prediction
Tareen Dawood, Chen Chen, Robin Andlauer +10
Evaluation of predictive deep learning (DL) models beyond conventional performance metrics has become increasingly important for applications in sensitive environments like healthc…
A Multimodal Deep Learning Model for Cardiac Resynchronisation Therapy Response Prediction
Esther Puyol-Antón, Baldeep S. Sidhu, Justin Gould +5
We present a novel multimodal deep learning framework for cardiac resynchronisation therapy (CRT) response prediction from 2D echocardiography and cardiac magnetic resonance (CMR)…
Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Prediction
Esther Puyol-Antón, Chen Chen, James R. Clough +9
Advances in deep learning (DL) have resulted in impressive accuracy in some medical image classification tasks, but often deep models lack interpretability. The ability of these mo…