  In terms of accuracy, deep learning (DL) models have had considerable success
in classification problems for medical imaging applications. However, it is
well-known that the outputs of such models, which typically utilise the SoftMax
function in the final classification layer can be over-confident, i.e. they are
poorly calibrated. Two competing solutions to this problem have been proposed:
uncertainty-aware training and evidential neural networks (ENNs). In this
paper, we perform an investigation into the improvements to model calibration
that can be achieved by each of these approaches individually, and their
combination. We perform experiments on two classification tasks: a simpler
MNIST digit classification task and a more complex and realistic medical
imaging artefact detection task using Phase Contrast Cardiac Magnetic Resonance
images. The experimental results demonstrate that model calibration can suffer
when the task becomes challenging enough to require a higher-capacity model.
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. The findings
highlight the potential use of these approaches to improve model calibration in
a complex application, which would in turn improve clinician trust in DL
models.
