  Improving calibration performance in deep learning (DL) classification models
is important when planning the use of DL in a decision-support setting. In such
a scenario, a confident wrong prediction could lead to a lack of trust and/or
harm in a high-risk application. We evaluate the impact on accuracy and
calibration of two types of approach that aim to improve DL classification
model calibration: deterministic uncertainty methods (DUM) and
uncertainty-aware training. Specifically, we test the performance of three DUMs
and two uncertainty-aware training approaches as well as their combinations. To
evaluate their utility, we use two realistic clinical applications from the
field of cardiac imaging: artefact detection from phase contrast cardiac
magnetic resonance (CMR) and disease diagnosis from the public ACDC CMR
dataset. Our results indicate that both DUMs and uncertainty-aware training can
improve both accuracy and calibration in both of our applications, with DUMs
generally offering the best improvements. We also investigate the combination
of the two approaches, resulting in a novel deterministic uncertainty-aware
training approach. This provides further improvements for some combinations of
DUMs and uncertainty-aware training approaches.
