  With the spread of COVID-19 over the world, the need arose for fast and
precise automatic triage mechanisms to decelerate the spread of the disease by
reducing human efforts e.g. for image-based diagnosis. Although the literature
has shown promising efforts in this direction, reported results do not consider
the variability of CT scans acquired under varying circumstances, thus
rendering resulting models unfit for use on data acquired using e.g. different
scanner technologies. While COVID-19 diagnosis can now be done efficiently
using PCR tests, this use case exemplifies the need for a methodology to
overcome data variability issues in order to make medical image analysis models
more widely applicable. In this paper, we explicitly address the variability
issue using the example of COVID-19 diagnosis and propose a novel generative
approach that aims at erasing the differences induced by e.g. the imaging
technology while simultaneously introducing minimal changes to the CT scans
through leveraging the idea of deep auto-encoders. The proposed prepossessing
architecture (PrepNet) (i) is jointly trained on multiple CT scan datasets and
(ii) is capable of extracting improved discriminative features for improved
diagnosis. Experimental results on three public datasets (SARS-COVID-2, UCSD
COVID-CT, MosMed) show that our model improves cross-dataset generalization by
up to $11.84$ percentage points despite a minor drop in within dataset
performance.
