Document Type
Article
Publication Date
8-9-2021
Abstract
The new coronavirus unleashed a worldwide pandemic in early 2020, and a fatality rate several times that of the flu. As the number of infections soared, and capabilities for testing lagged behind, chest X-ray (CXR) imaging became more relevant in the early diagnosis and treatment planning for patients with suspected or confirmed COVID-19 infection. In a few weeks, proposed new methods for lung screening using deep learning rapidly appeared, while quality assurance discussions lagged behind. This paper proposes a set of protocols to validate deep learning algorithms, including our ROI Hide-and-Seek protocol, which emphasizes or hides key regions of interest from CXR data. Our protocol allows assessing the classification performance for anomaly detection and its correlation to radiological signatures, an important issue overlooked in several deep learning approaches proposed so far. By running a set of systematic tests over CXR representations using public image datasets, we demonstrate the weaknesses of current techniques and offer perspectives on the advantages and limitations of automated radiography analysis when using heterogeneous data sources.
Recommended Citation
Sadre, Robbie; Sundaram, Baskaran; Majumdar, Sharmila; and Ushizima, Daniela, "Validating deep learning inference during chest X-ray classification for COVID-19 screening." (2021). Department of Radiology Faculty Papers. Paper 111.
https://jdc.jefferson.edu/radiologyfp/111
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
PubMed ID
34373530
Language
English
Comments
This article is the authors’ final published version in Scientific Reports, Volume 11, Issue 1, August 2021, Article number 16075.
The published version is available at https://doi.org/10.1038/s41598-021-95561-y. Copyright © Sadre et al.