  The coronavirus pandemic has been going on since the year 2019, and the trend
is still not abating. Therefore, it is particularly important to classify
medical CT scans to assist in medical diagnosis. At present, Supervised Deep
Learning algorithms have made a great success in the classification task of
medical CT scans, but medical image datasets often require professional image
annotation, and many research datasets are not publicly available. To solve
this problem, this paper is inspired by the self-supervised learning algorithm
MAE and uses the MAE model pre-trained on ImageNet to perform transfer learning
on CT Scans dataset. This method improves the generalization performance of the
model and avoids the risk of overfitting on small datasets. Through extensive
experiments on the COVID-CT dataset and the SARS-CoV-2 dataset, we compare the
SSL-based method in this paper with other state-of-the-art supervised
learning-based pretraining methods. Experimental results show that our method
improves the generalization performance of the model more effectively and
avoids the risk of overfitting on small datasets. The model achieved almost the
same accuracy as supervised learning on both test datasets. Finally, ablation
experiments aim to fully demonstrate the effectiveness of our method and how it
works.
