Classification of COVID-19 in chest radiographs: assessing the impact of imaging parameters using clinical and simulated images
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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.
Published Version (Please cite this version)10.1117/12.2582223
Publication InfoFricks, 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.
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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
Research Associate, Senior
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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