Uncertainty Aware Training to Improve Deep Learning Model Calibration for Classification of Cardiac MR Images

no code implementations • 29 Aug 2023 • Tareen Dawood, Chen Chen, Baldeep S. Sidhua, Bram Ruijsink, Justin Goulda, Bradley Porter, Mark K. Elliott, Vishal Mehta, Christopher A. Rinaldi, Esther Puyol-Anton, Reza Razavi, Andrew P. King

The best-performing model in terms of both classification accuracy and the most common calibration measure, expected calibration error (ECE) was the Confidence Weight method, a novel approach that weights the loss of samples to explicitly penalise confident incorrect predictions.

Addressing Deep Learning Model Calibration Using Evidential Neural Networks and Uncertainty-Aware Training

no code implementations • 30 Jan 2023 • Tareen Dawood, Emily Chan, Reza Razavi, Andrew P. King, Esther Puyol-Anton

However, in our complex artefact detection task, we saw an improvement in calibration for both a low and higher-capacity model when implementing both the ENN and uncertainty-aware training together, indicating that this approach can offer a promising way to improve calibration in such settings.

Classification

Uncertainty-Aware Training for Cardiac Resynchronisation Therapy Response Prediction

no code implementations • 22 Sep 2021 • Tareen Dawood, Chen Chen, Robin Andlauer, Baldeep S. Sidhu, Bram Ruijsink, Justin Gould, Bradley Porter, Mark Elliott, Vishal Mehta, C. Aldo Rinaldi, Esther Puyol-Antón, Reza Razavi, Andrew P. King

Evaluation of predictive deep learning (DL) models beyond conventional performance metrics has become increasingly important for applications in sensitive environments like healthcare.