  Successful data representation is a fundamental factor in machine learning
based medical imaging analysis. Deep Learning (DL) has taken an essential role
in robust representation learning. However, the inability of deep models to
generalize to unseen data can quickly overfit intricate patterns. Thereby, we
can conveniently implement strategies to aid deep models in discovering useful
priors from data to learn their intrinsic properties. Our model, which we call
a dual role network (DRN), uses a dependency maximization approach based on
Least Squared Mutual Information (LSMI). The LSMI leverages dependency measures
to ensure representation invariance and local smoothness. While prior works
have used information theory measures like mutual information, known to be
computationally expensive due to a density estimation step, our LSMI
formulation alleviates the issues of intractable mutual information estimation
and can be used to approximate it. Experiments on CT based COVID-19 Detection
and COVID-19 Severity Detection benchmarks demonstrate the effectiveness of our
method.
