  Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic in over 200
countries and territories, which has resulted in a great public health concern
across the international community. Analysis of X-ray imaging data can play a
critical role in timely and accurate screening and fighting against COVID-19.
Supervised deep learning has been successfully applied to recognize COVID-19
pathology from X-ray imaging datasets. However, it requires a substantial
amount of annotated X-ray images to train models, which is often not applicable
to data analysis for emerging events such as COVID-19 outbreak, especially in
the early stage of the outbreak. To address this challenge, this paper proposes
a two-path semi-supervised deep learning model, ssResNet, based on Residual
Neural Network (ResNet) for COVID-19 image classification, where two paths
refer to a supervised path and an unsupervised path, respectively. Moreover, we
design a weighted supervised loss that assigns higher weight for the minority
classes in the training process to resolve the data imbalance. Experimental
results on a large-scale of X-ray image dataset COVIDx demonstrate that the
proposed model can achieve promising performance even when trained on very few
labeled training images.
