  The recent trend of integrating multi-source Chest X-Ray datasets to improve
automated diagnostics raises concerns that models learn to exploit
source-specific correlations to improve performance by recognizing the source
domain of an image rather than the medical pathology. We hypothesize that this
effect is enforced by and leverages label-imbalance across the source domains,
i.e, prevalence of a disease corresponding to a source. Therefore, in this
work, we perform a thorough study of the effect of label-imbalance in
multi-source training for the task of pneumonia detection on the widely used
ChestX-ray14 and CheXpert datasets. The results highlight and stress the
importance of using more faithful and transparent self-explaining models for
automated diagnosis, thus enabling the inherent detection of spurious learning.
They further illustrate that this undesirable effect of learning spurious
correlations can be reduced considerably when ensuring label-balanced source
domain datasets.
