Targeted Self Supervision for Classification on a Small COVID-19 CT Scan Dataset

TL;DR

This study demonstrates that targeted self supervision significantly improves COVID-19 CT scan classification accuracy on small datasets, offering a promising approach for medical imaging with limited labeled data.

Contribution

The paper introduces targeted self supervision, a novel strategy that enhances classification performance on small COVID-19 CT datasets compared to traditional methods.

Findings

Almost 8% accuracy increase with full self supervision

Self supervision outperforms non-self supervised methods

Effective on small, limited labeled datasets

Abstract

Traditionally, convolutional neural networks need large amounts of data labelled by humans to train. Self supervision has been proposed as a method of dealing with small amounts of labelled data. The aim of this study is to determine whether self supervision can increase classification performance on a small COVID-19 CT scan dataset. This study also aims to determine whether the proposed self supervision strategy, targeted self supervision, is a viable option for a COVID-19 imaging dataset. A total of 10 experiments are run comparing the classification performance of the proposed method of self supervision with different amounts of data. The experiments run with the proposed self supervision strategy perform significantly better than their non-self supervised counterparts. We get almost 8% increase in accuracy with full self supervision when compared to no self supervision. The results suggest that self supervision can improve classification performance on a small COVID-19 CT scan dataset. Code for targeted self supervision can be found at this link: https://github.com/Mewtwo/Targeted-Self-Supervision/tree/main/COVID-CT