  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
