SSL-MedImNet: Self-Supervised Pre-training of Deep Neural Network for COVID-19 Diagnosis

Abstract

This paper applies self-supervised learning to diagnose coronavirus disease (COVID-19) among other pneumonia and normal cases based on chest Computed Tomography (CT) images. Being aware that medical imaging in real-world scenarios lacks well-verified and explicitly labeled datasets, which is known as a big challenge for supervised learning, we utilize Momentum Contrast v2 (MoCo v2) algorithm to pre-train our proposed Self-Supervised Medical Imaging Network (SSL-MedImNet) with remarkable generalization from substantial unlabeled data. The proposed model achieves competitive and promising performance in COVIDx CT-2, which is a well-known and high-quality dataset for COVID-19 assessment. Besides, its pre-trained representations can be transferred well for the diagnosis task. Moreover, SSL-MedImNet approximately matches its supervised candidates COVID-Net CT-1 and COVID-Net CT-2 by small distinctions. In particular, with only some additional dense layers, the proposed model achieves COVID-19 accuracy of 88.3% and specificity of 98.4% approximately, and competitive results for normal and pneumonia cases. The results advocate the potential of self-supervised learning to accomplish highly generalized understanding from unlabeled medical images and then transfer it for relevant supervised tasks in real scenarios.