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.
Original language | English |
---|---|
Title of host publication | BIOIMAGING 2021 - 8th International Conference on Bioimaging; Part of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2021 |
Editors | Alexandre Douplik, Ana Fred, Hugo Gamboa |
Publisher | SciTePress |
Pages | 65-72 |
Number of pages | 8 |
ISBN (Electronic) | 9789897584909 |
State | Published - 2021 |
Event | 8th International Conference on Bioimaging, BIOIMAGING 2021 - Part of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2021 - Virtual, Online Duration: Feb 11 2021 → Feb 13 2021 |
Publication series
Name | BIOIMAGING 2021 - 8th International Conference on Bioimaging; Part of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2021 |
---|
Conference
Conference | 8th International Conference on Bioimaging, BIOIMAGING 2021 - Part of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2021 |
---|---|
City | Virtual, Online |
Period | 02/11/21 → 02/13/21 |
Funding
a https://orcid.org/0000-0001-8052-7416 ∗ Biomedical Science, Engineering, and Computing Group, Oak Ridge National Laboratory, Oak Ridge, USA †This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of the manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). This research is sponsored in whole or in part by the AI Initiative (LOIS 9613) and Privacy research (LOIS 9831) as part of the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory.
Keywords
- Chest radiographs
- Computer-aided diagnosis of lung conditions
- Domain-shift detection and removal