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Deep neural networks have revolutionized medical image analysis and diseasediagnosis. Despite their impressive performance, it is difficult to generatewell-calibrated probabilistic outputs for such networks, which makes themuninterpretable black boxes. Bayesian neural networks provide a principledapproach for modelling uncertainty and increasing patient safety, but they havea large computational overhead and provide limited improvement in calibration.In this work, by taking skin lesion classification as an example task, we showthat by shifting Bayesian inference to the functional space we can craftmeaningful priors that give better calibrated uncertainty estimates at a muchlower computational cost.

Functional Space Variational Inference for Uncertainty Estimation in Computer Aided Diagnosis