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

arXiv: https://arxiv.org/abs/2011.10188 · HTML: https://mlsanity.com/paper/2011.10188/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2020.

## Built on by (most cited first, 8 of 8)

- [A k-mer Based Approach for SARS-CoV-2 Variant Identification](https://mlsanity.com/paper/2108.03465/) (2021)
- [Self-Supervised Pre-Training with Contrastive and Masked Autoencoder Methods for Dealing with Small Datasets in Deep Learning for Medical Imaging](https://mlsanity.com/paper/2308.06534/) (2023)
- [Visual Transformer with Statistical Test for COVID-19 Classification](https://mlsanity.com/paper/2107.05334/) (2021)
- [Online unsupervised Learning for domain shift in COVID-19 CT scan datasets](https://mlsanity.com/paper/2108.02002/) (2021)
- [A Survey of the Impact of Self-Supervised Pretraining for Diagnostic Tasks with Radiological Images](https://mlsanity.com/paper/2309.02555/) (2023)
- [Multi-scale alignment and Spatial ROI Module for COVID-19 Diagnosis](https://mlsanity.com/paper/2207.01345/) (2022)
- [Deep Learning Approaches for Medical Imaging Under Varying Degrees of Label Availability: A Comprehensive Survey](https://mlsanity.com/paper/2504.11588/) (2025)
- [Structured Output Regularization: a framework for few-shot transfer learning](https://mlsanity.com/paper/2510.08728/) (2025)

## Abstract (arXiv)

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