Self-supervised Model Based on Masked Autoencoders Advance CT Scans Classification

TL;DR

This paper introduces a self-supervised learning approach using Masked Autoencoders pre-trained on ImageNet to classify CT scans, reducing the need for annotated data and improving generalization on small datasets.

Contribution

It demonstrates that self-supervised pretraining with MAE enhances CT scan classification accuracy, matching supervised methods without requiring extensive annotations.

Findings

Improved generalization performance on COVID-CT and SARS-CoV-2 datasets.

Achieved accuracy comparable to supervised learning methods.

Effective in avoiding overfitting on small datasets.

Abstract

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.