  This work provides a strong baseline for the problem of multi-source
multi-target domain adaptation and generalization in medical imaging. Using a
diverse collection of ten chest X-ray datasets, we empirically demonstrate the
benefits of training medical imaging deep learning models on varied patient
populations for generalization to out-of-sample domains.
