Role of standard and soft tissue chest radiography images in COVID-19 diagnosis using deep learning

Qiyuan Hu; Karen Drukker; Maryellen L. Giger

doi:10.1117/12.2581977

15 February 2021 Role of standard and soft tissue chest radiography images in COVID-19 diagnosis using deep learning

Qiyuan Hu, Karen Drukker, Maryellen L. Giger

Proceedings Volume 11597, Medical Imaging 2021: Computer-Aided Diagnosis; 1159704 (2021) https://doi.org/10.1117/12.2581977
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

In this study, we aim to investigate the role of standard and soft tissue chest radiography (CXR) images in the task of COVID-19 diagnosis at patient presentation using deep learning. The dataset consisted of the initial CXR exams of 6687 patients after their reverse transcription polymerase chain reaction tests for the SARS-CoV-2 virus, 1040 (15.6%) of whom tested positive and 5647 (84.4%) of whom tested negative for COVID-19. Each CXR exam contained a standard image and a soft tissue image obtained either from dual-energy acquisition or postprocessing technology. A curriculum learning technique was employed to train the model on a sequence of gradually more specific and complex tasks by first fine-tuning a model optimized for natural images on a previously established CXR dataset to diagnose a broad spectrum of pathologies, then refining the model on another established dataset to detect pneumonia, and finally fine-tuning the model again on the COVID-19 dataset collected for this study. In the last phase of training, the COVID-19 positive/negative classification was performed on 1) the standard images, 2) the soft tissue images, and 3) the two combined via feature fusion. The classification performances were evaluated on a held-out test set of 1338 cases with the same disease prevalence as training and validation sets using the area under the receiver operating characteristic curve (AUC). The three classification schemes with different inputs overall yielded AUC values of 0.76 [0.72, 0.80], 0.76 [0.73, 0.80], and 0.76 [0.72, 0.79]. When compared using the DeLong test, the three schemes yielded equivalent performances with an equivalence margin of ∆AUC = 0.05 was chosen prima facie. The value of the inclusion of soft tissue images will continue to be investigated in the segmentation and feature extraction of COVID-19 involvement, which may contribute to improving the performance of COVID-19 early diagnosis.

Conference Presentation

Citation Download Citation

Qiyuan Hu, Karen Drukker, and Maryellen L. Giger "Role of standard and soft tissue chest radiography images in COVID-19 diagnosis using deep learning", Proc. SPIE 11597, Medical Imaging 2021: Computer-Aided Diagnosis, 1159704 (15 February 2021); https://doi.org/10.1117/12.2581977

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