  Deep learning (DL) models are being deployed at medical centers to aid
radiologists for diagnosis of lung conditions from chest radiographs. Such
models are often trained on a large volume of publicly available labeled
radiographs. These pre-trained DL models' ability to generalize in clinical
settings is poor because of the changes in data distributions between publicly
available and privately held radiographs. In chest radiographs, the
heterogeneity in distributions arises from the diverse conditions in X-ray
equipment and their configurations used for generating the images. In the
machine learning community, the challenges posed by the heterogeneity in the
data generation source is known as domain shift, which is a mode shift in the
generative model. In this work, we introduce a domain-shift detection and
removal method to overcome this problem. Our experimental results show the
proposed method's effectiveness in deploying a pre-trained DL model for
abnormality detection in chest radiographs in a clinical setting.
