Self-supervised Learning for Small Shot COVID-19 Classification
Abstract
Recently, COVID-19 has become one of the most severe and widespread diseases with an increasing number of infections and deaths. An accurate and high-speed automatic classifier will increase the efficiency of diagnosis and reduce fatigue misdiagnosis. Given the contradiction that many previous classifiers require a large amount of data for training while it is difficult to collect the medical images of COVID-19 with labels, we propose a classification model based on self-supervised learning and transfer learning, which uses rotation and division as labels and then transfers the parameters to the classifier. It solves the overfitting problem caused by insufficient data set and improves the accuracy by nearly 30%
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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 [46]
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Huang, S.-C., Pareek, A., Jensen, M., Lungren, M. P., Yeung, S., & Chaudhari, A. S. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00811-0 · Cited by 479 · see citing works
- Authors
- Zhu, Y.
- Affiliations
- Southern University of Science and Technology (CN)
- Corresponding author
- Zhu, Yujie
- ORCID
- Zhu, Yujie
- Venue
- 2021 3rd International Conference on Information Technology and Computer Communications
- Publisher
- ACM
- Published
- 2021-06-23
- Pages
- 36-40
- OpenAlex
- W3203445487
- Cited by
- 2
- References
- 10
- Citations by year
- 2023: 2
- FWCI
- 0.63
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Information Technology and Computer Communications (ITCC) (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
- COVID-19 classification, self-supervised learning, transfer learning, chest X-ray images, overfitting, medical image classification
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