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Authors: Abhishek K. Dubey ; Michael T. Young ; Christopher Stanley ; Dalton Lunga and Jacob Hinkle

Affiliation: Oak Ridge National Laboratory, Oak Ridge, TN, U.S.A.

Keyword(s): Computer-aided Diagnosis of Lung Conditions, Domain-shift Detection and Removal, Chest Radiographs.

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. (More)

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Paper citation in several formats:
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 (BIOSTEC 2021) - BIOIMAGING; ISBN 978-989-758-490-9; ISSN 2184-4305, SciTePress, pages 65-72. DOI: 10.5220/0010302500002865

@conference{bioimaging21,
author={Abhishek K. Dubey. and Michael T. 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 (BIOSTEC 2021) - BIOIMAGING},
year={2021},
pages={65-72},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010302500002865},
isbn={978-989-758-490-9},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - BIOIMAGING
TI - Computer-aided Abnormality Detection in Chest Radiographs in a Clinical Setting via Domain-adaptation
SN - 978-989-758-490-9
IS - 2184-4305
AU - Dubey, A.
AU - Young, M.
AU - Stanley, C.
AU - Lunga, D.
AU - Hinkle, J.
PY - 2021
SP - 65
EP - 72
DO - 10.5220/0010302500002865
PB - SciTePress

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