  Artificial intelligence has made great progress in medical data analysis, but
the lack of robustness and trustworthiness has kept these methods from being
widely deployed. As it is not possible to train networks that are accurate in
all scenarios, models must recognize situations where they cannot operate
confidently. Bayesian deep learning methods sample the model parameter space to
estimate uncertainty, but these parameters are often subject to the same
vulnerabilities, which can be exploited by adversarial attacks. We propose a
novel ensemble approach based on feature decorrelation and Fourier partitioning
for teaching networks diverse complementary features, reducing the chance of
perturbation-based fooling. We test our approach on single and multi-channel
electrocardiogram classification, and adapt adversarial training and DVERGE
into the Bayesian ensemble framework for comparison. Our results indicate that
the combination of decorrelation and Fourier partitioning generally maintains
performance on unperturbed data while demonstrating superior robustness and
uncertainty estimation on projected gradient descent and smooth adversarial
attacks of various magnitudes. Furthermore, our approach does not require
expensive optimization with adversarial samples, adding much less compute to
the training process than adversarial training or DVERGE. These methods can be
applied to other tasks for more robust and trustworthy models.
