Online Unsupervised Learning For Domain Shift In Covid-19 CT Scan Datasets
Published version of a preprint
Abstract
Neural networks often require large amounts of expert annotated data to train. When changes are made in the process of medical imaging, trained networks may not perform as well, and obtaining large amounts of expert annotations for each change in the imaging process can be time consuming and expensive. Online unsupervised learning is a method that has been proposed to deal with situations where there is a domain shift in incoming data, and a lack of annotations. The aim of this study is to see whether online unsupervised learning can help COVID-19 CT scan classification models adjust to slight domain shifts, when there are no annotations available for the new data. A total of six experiments are performed using three test datasets with differing amounts of domain shift. These experiments compare the performance of the online unsupervised learning strategy to a baseline, as well as comparing how the strategy performs on different domain shifts. Code for online unsupervised learning can be found at this link: https://github.com/Mewtwo/online-unsupervised-learning
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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 [45]
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“…domain is likely a combination of the availability of large-scale public chest datasets such as CheXpert 73 , RSPECT 74 , RadFusion 75 and MIMIC-CXR 76 , as well as the motivation to solve acute or emerging healthcare threats, which was the case during the coronavirus pandemic 45,46,48,67,77 – 83 .”
Background
- Authors
- Ewen, N., & Khan, N.
- Affiliations
- Toronto Metropolitan University (CA)
- ORCID
- Khan, Naimul
- Venue
- 2021 IEEE International Conference on Autonomous Systems (ICAS)
- Publisher
- IEEE
- Published
- 2021-08-11
- Pages
- 1-5
- OpenAlex
- W3204802110
- Cited by
- 5
- References
- 15
- Citations by year
- 2023: 5
- FWCI
- 0.58
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE International Conference on Autonomous Systems (ICAS) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Physical Sciences › Computer Science › Artificial Intelligence › Data Stream Mining Techniques
- Keywords
- online unsupervised learning, domain shift, COVID-19 pandemic, CT scan classification, medical image classification, annotated data scarcity, continual learning, radiology
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