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

• URL: http://arxiv.org/abs/2012.10564v1
• Date: Sat, 19 Dec 2020 01:01:48 GMT
• Title: Computer-aided abnormality detection in chest radiographs in a clinical setting via domain-adaptation
• Authors: Abhishek K Dubey, Michael T Young, Christopher Stanley, Dalton Lunga, Jacob Hinkle
• Abstract summary: Deep learning (DL) models are being deployed at medical centers to aid radiologists for diagnosis of lung conditions from chest 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 this work, we introduce a domain-shift detection and removal method to overcome this problem.
• Score: 0.23624125155742057
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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