  Evaluation of predictive deep learning (DL) models beyond conventional
performance metrics has become increasingly important for applications in
sensitive environments like healthcare. Such models might have the capability
to encode and analyse large sets of data but they often lack comprehensive
interpretability methods, preventing clinical trust in predictive outcomes.
Quantifying uncertainty of a prediction is one way to provide such
interpretability and promote trust. However, relatively little attention has
been paid to how to include such requirements into the training of the model.
In this paper we: (i) quantify the data (aleatoric) and model (epistemic)
uncertainty of a DL model for Cardiac Resynchronisation Therapy response
prediction from cardiac magnetic resonance images, and (ii) propose and perform
a preliminary investigation of an uncertainty-aware loss function that can be
used to retrain an existing DL image-based classification model to encourage
confidence in correct predictions and reduce confidence in incorrect
predictions. Our initial results are promising, showing a significant increase
in the (epistemic) confidence of true positive predictions, with some evidence
of a reduction in false negative confidence.
