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Deep learning (DL) models are being deployed at medical centers to aidradiologists for diagnosis of lung conditions from chest radiographs. Suchmodels are often trained on a large volume of publicly available labeledradiographs. These pre-trained DL models' ability to generalize in clinicalsettings is poor because of the changes in data distributions between publiclyavailable and privately held radiographs. In chest radiographs, theheterogeneity in distributions arises from the diverse conditions in X-rayequipment and their configurations used for generating the images. In themachine learning community, the challenges posed by the heterogeneity in thedata generation source is known as domain shift, which is a mode shift in thegenerative model. In this work, we introduce a domain-shift detection andremoval method to overcome this problem. Our experimental results show theproposed method's effectiveness in deploying a pre-trained DL model forabnormality detection in chest radiographs in a clinical setting.

Computer-aided abnormality detection in chest radiographs in a clinical setting via domain-adaptation