Computer-aided Abnormality Detection in Chest Radiographs in a Clinical Setting via Domain-adaptation

Abhishek Dubey, Michael Young, Christopher Stanley, Dalton Lunga, Jacob Hinkle

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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Paper Citation


in Harvard Style

Dubey A., Young M., Stanley C., Lunga D. and Hinkle J. (2021). Computer-aided Abnormality Detection in Chest Radiographs in a Clinical Setting via Domain-adaptation.In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING, ISBN 978-989-758-490-9, pages 65-72. DOI: 10.5220/0010302500650072


in Bibtex Style

@conference{bioimaging21,
author={Abhishek Dubey and Michael Young and Christopher Stanley and Dalton Lunga and Jacob Hinkle},
title={Computer-aided Abnormality Detection in Chest Radiographs in a Clinical Setting via Domain-adaptation},
booktitle={Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING,},
year={2021},
pages={65-72},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010302500650072},
isbn={978-989-758-490-9},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING,
TI - Computer-aided Abnormality Detection in Chest Radiographs in a Clinical Setting via Domain-adaptation
SN - 978-989-758-490-9
AU - Dubey A.
AU - Young M.
AU - Stanley C.
AU - Lunga D.
AU - Hinkle J.
PY - 2021
SP - 65
EP - 72
DO - 10.5220/0010302500650072