A Strong Baseline for Domain Adaptation and Generalization in Medical Imaging
This work provides a baseline for multi-source multi-target domain adaptation in medical imaging, which is incremental as it builds on existing methods without introducing new paradigms.
The authors tackled the problem of domain adaptation and generalization in medical imaging by training deep learning models on diverse chest X-ray datasets, resulting in improved generalization to out-of-sample domains as demonstrated empirically.
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.