Online unsupervised Learning for domain shift in COVID-19 CT scan datasets

TL;DR

This paper investigates the effectiveness of online unsupervised learning in adapting COVID-19 CT scan classification models to domain shifts without requiring additional annotations.

Contribution

It demonstrates that online unsupervised learning can help models adjust to slight domain shifts in COVID-19 CT scans without needing new annotations.

Findings

Online unsupervised learning improves model adaptation to domain shifts.

Performance varies with the degree of domain shift.

The method offers a practical solution for real-time model adjustment.

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