Classification of COVID-19 in chest radiographs: assessing the impact of imaging parameters using clinical and simulated images
Abstract
As computer-aided diagnostics develop to address new challenges in medical imaging,
including emerging diseases such
as COVID-19, the initial development is hampered by availability of imaging data.
Deep learning algorithms are
particularly notorious for performance that tends to improve proportionally to the
amount of available data. Simulated
images, as available through advanced virtual trials, may present an alternative in
data-constrained applications. We begin
with our previously trained COVID-19 x-ray classification model (denoted as CVX) that
leveraged additional training
with existing pre-pandemic chest radiographs to improve classification performance
in a set of COVID-19 chest
radiographs. The CVX model achieves demonstrably better performance on clinical images
compared to an equivalent
model that applies standard transfer learning from ImageNet weights. The higher performing
CVX model is then shown
to generalize effectively to a set of simulated COVID-19 images, both quantitative
comparisons of AUCs from clinical to
simulated image sets, but also in a qualitative sense where saliency map patterns
are consistent when compared between
sets. We then stratify the classification results in simulated images to examine dependencies
in imaging parameters when
patient features are constant. Simulated images show promise in optimizing imaging
parameters for accurate classification
in data-constrained applications.
Type
Journal articlePermalink
https://hdl.handle.net/10161/22421Published Version (Please cite this version)
10.1117/12.2582223Publication Info
Fricks, Rafael; Abadi, Ehsan; Ria, Francesco; & Samei, Ehsan (2021). Classification of COVID-19 in chest radiographs: assessing the impact of imaging parameters
using clinical and simulated images. Medical Imaging 2021: Computer-Aided Diagnosis, 115970A. pp. 1-11. 10.1117/12.2582223. Retrieved from https://hdl.handle.net/10161/22421.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Ehsan Abadi
Assistant Professor in Radiology
Ehsan Abadi, PhD is an imaging scientist at Duke University. He serves as an Assistant
Professor in the departments of Radiology and Electrical & Computer Engineering, a
faculty member in the Medical Physics Graduate Program and Carl E. Ravin Advanced
Imaging Laboratories, and a co-Lead in the Center for Virtual Imaging Trials. Ehsan’s
research focuses on quantitative imaging and optimization, CT imaging, lung diseases,
computational human modeling, and medical imag
Francesco Ria
Research Associate, Senior
Ehsan Samei
Reed and Martha Rice Distinguished Professor of Radiology
Dr. Ehsan Samei, PhD, DABR, FAAPM, FSPIE, FAIMBE, FIOMP, FACR is a Persian-American
medical physicist. He is a tenured Professor of Radiology, Medical Physics, Biomedical
Engineering, Physics, and Electrical and Computer Engineering at Duke University,
where he also serves as the Chief Imaging Physicist for Duke University Health System,
the director of the Carl E Ravin Advanced Imaging Laboratories, and the director of
Center for Virtual Imaging Trials. He is certi
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