Targeted Self Supervision For Classification On A Small Covid-19 Ct Scan Dataset
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 6% increase on average with self supervision compared to no self supervision, and more than 8% increase in accuracy in our best run with 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-SelfSupervision/tree/main/COVID-CT.
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Nine of the innate relationship studies designed their pre-text task based on different image transformations, including rotation prediction, horizontal flip prediction, reordering shuffled slices, and patch order prediction.
“Nine of these studies designed their innate relationship pre-text task based on different image transformations, including rotation prediction44–47, horizontal flip prediction48, reordering shuffled slices49, and patch order prediction46,50–52.”
From Self-supervised learning for medical image classification: a systematic review and implementation guidelines (2023) · cites this paper as [48]
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Self-citation
- Authors
- Ewen, N., & Khan, N.
- Affiliations
- Toronto Metropolitan University (CA)
- ORCID
- Khan, Naimul
- Venue
- 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)
- Publisher
- IEEE
- Published
- 2021-04-13
- Pages
- 1481-1485
- OpenAlex
- W3164251887
- Cited by
- 29
- References
- 14
- Citations by year
- 2025: 1 · 2024: 5 · 2023: 7 · 2022: 4 · 2021: 12
- FWCI
- 8.80
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE International Symposium on Biomedical Imaging (ISBI) (publishedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- self-supervised learning, COVID-19 pandemic, CT scan classification, convolutional neural networks, medical image classification, data augmentation
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