  Deep neural networks have revolutionized medical image analysis and disease
diagnosis. Despite their impressive performance, it is difficult to generate
well-calibrated probabilistic outputs for such networks, which makes them
uninterpretable black boxes. Bayesian neural networks provide a principled
approach for modelling uncertainty and increasing patient safety, but they have
a large computational overhead and provide limited improvement in calibration.
In this work, by taking skin lesion classification as an example task, we show
that by shifting Bayesian inference to the functional space we can craft
meaningful priors that give better calibrated uncertainty estimates at a much
lower computational cost.
