  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
