  The novel 2019 Coronavirus (COVID-19) infection has spread world widely and
is currently a major healthcare challenge around the world. Chest Computed
Tomography (CT) and X-ray images have been well recognized to be two effective
techniques for clinical COVID-19 disease diagnoses. Due to faster imaging time
and considerably lower cost than CT, detecting COVID-19 in chest X-ray (CXR)
images is preferred for efficient diagnosis, assessment and treatment. However,
considering the similarity between COVID-19 and pneumonia, CXR samples with
deep features distributed near category boundaries are easily misclassified by
the hyper-planes learned from limited training data. Moreover, most existing
approaches for COVID-19 detection focus on the accuracy of prediction and
overlook the uncertainty estimation, which is particularly important when
dealing with noisy datasets. To alleviate these concerns, we propose a novel
deep network named {\em RCoNet$^k_s$} for robust COVID-19 detection which
employs {\em Deformable Mutual Information Maximization} (DeIM), {\em Mixed
High-order Moment Feature} (MHMF) and {\em Multi-expert Uncertainty-aware
Learning} (MUL). With DeIM, the mutual information (MI) between input data and
the corresponding latent representations can be well estimated and maximized to
capture compact and disentangled representational characteristics. Meanwhile,
MHMF can fully explore the benefits of using high-order statistics and extract
discriminative features of complex distributions in medical imaging. Finally,
MUL creates multiple parallel dropout networks for each CXR image to evaluate
uncertainty and thus prevent performance degradation caused by the noise in the
data.
