  Radiologist examination of chest CT is an effective way for screening
COVID-19 cases. In this work, we overcome three challenges in the automation of
this process: (i) the limited number of supervised positive cases, (ii) the
lack of region-based supervision, and (iii) the variability across acquisition
sites. These challenges are met by incorporating a recent augmentation solution
called SnapMix, by a new patch embedding technique, and by performing a
test-time stability analysis. The three techniques are complementary and are
all based on utilizing the heatmaps produced by the Class Activation Mapping
(CAM) explainability method. Compared to the current state of the art, we
obtain an increase of five percent in the F1 score on a site with a relatively
high number of cases, and a gap twice as large for a site with much fewer
training images.
