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.
CITATION STYLE
Hoang, T. N. M., Son, T. T., Nghiem, N. D., & Tuan, L. M. (2022). SSL-MedImNet: Self-Supervised Pre-training of Deep Neural Network for COVID-19 Diagnosis. In Lecture Notes on Data Engineering and Communications Technologies (Vol. 148, pp. 406–415). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-15063-0_39
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