Self-supervised Model Based on Masked Autoencoders Advance CT Scans Classification
This work addresses the challenge of limited annotated medical image datasets for researchers and clinicians, though it is incremental as it applies an existing self-supervised method to a new domain.
The paper tackled the problem of classifying medical CT scans for COVID-19 diagnosis by using a self-supervised learning method based on Masked Autoencoders (MAE) pre-trained on ImageNet, achieving accuracy comparable to supervised learning on COVID-CT and SARS-CoV-2 datasets.
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.