  Over the last years, Deep Learning has been successfully applied to a broad
range of medical applications. Especially in the context of chest X-ray
classification, results have been reported which are on par, or even superior
to experienced radiologists. Despite this success in controlled experimental
environments, it has been noted that the ability of Deep Learning models to
generalize to data from a new domain (with potentially different tasks) is
often limited. In order to address this challenge, we investigate techniques
from the field of Continual Learning (CL) including Joint Training (JT),
Elastic Weight Consolidation (EWC) and Learning Without Forgetting (LWF). Using
the ChestX-ray14 and the MIMIC-CXR datasets, we demonstrate empirically that
these methods provide promising options to improve the performance of Deep
Learning models on a target domain and to mitigate effectively catastrophic
forgetting for the source domain. To this end, the best overall performance was
obtained using JT, while for LWF competitive results could be achieved - even
without accessing data from the source domain.
