  Computed tomography (CT) and chest X-ray (CXR) have been the two dominant
imaging modalities deployed for improved management of Coronavirus disease 2019
(COVID-19). Due to faster imaging, less radiation exposure, and being
cost-effective CXR is preferred over CT. However, the interpretation of CXR
images, compared to CT, is more challenging due to low image resolution and
COVID-19 image features being similar to regular pneumonia. Computer-aided
diagnosis via deep learning has been investigated to help mitigate these
problems and help clinicians during the decision-making process. The
requirement for a large amount of labeled data is one of the major problems of
deep learning methods when deployed in the medical domain. To provide a
solution to this, in this work, we propose a semi-supervised learning (SSL)
approach using minimal data for training. We integrate local-phase CXR image
features into a multi-feature convolutional neural network architecture where
the training of SSL method is obtained with a teacher/student paradigm.
Quantitative evaluation is performed on 8,851 normal (healthy), 6,045
pneumonia, and 3,795 COVID-19 CXR scans. By only using 7.06% labeled and 16.48%
unlabeled data for training, 5.53% for validation, our method achieves 93.61\%
mean accuracy on a large-scale (70.93%) test data. We provide comparison
results against fully supervised and SSL methods. Code:
https://github.com/endiqq/Multi-Feature-Semi-Supervised-Learning-for-COVID-19-CXR-Images

Image and Video Processing