ArXiv Preprint
Purpose: Considering several patients screened due to COVID-19 pandemic,
computer-aided detection has strong potential in assisting clinical workflow
efficiency and reducing the incidence of infections among radiologists and
healthcare providers. Since many confirmed COVID-19 cases present radiological
findings of pneumonia, radiologic examinations can be useful for fast
detection. Therefore, chest radiography can be used to fast screen COVID-19
during the patient triage, thereby determining the priority of patient's care
to help saturated medical facilities in a pandemic situation. Methods: In this
paper, we propose a new learning scheme called self-supervised transfer
learning for detecting COVID-19 from chest X-ray (CXR) images. We compared six
self-supervised learning (SSL) methods (Cross, BYOL, SimSiam, SimCLR,
PIRL-jigsaw, and PIRL-rotation) with the proposed method. Additionally, we
compared six pretrained DCNNs (ResNet18, ResNet50, ResNet101, CheXNet,
DenseNet201, and InceptionV3) with the proposed method. We provide quantitative
evaluation on the largest open COVID-19 CXR dataset and qualitative results for
visual inspection. Results: Our method achieved a harmonic mean (HM) score of
0.985, AUC of 0.999, and four-class accuracy of 0.953. We also used the
visualization technique Grad-CAM++ to generate visual explanations of different
classes of CXR images with the proposed method to increase the
interpretability. Conclusions: Our method shows that the knowledge learned from
natural images using transfer learning is beneficial for SSL of the CXR images
and boosts the performance of representation learning for COVID-19 detection.
Our method promises to reduce the incidence of infections among radiologists
and healthcare providers.
Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama
2022-12-19
