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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
In this worldwide spread of SARS-CoV-2 (COVID-19) infection, it is of utmost importance to detect the disease at an early stage especially in the hot spots of this epidemic. There are more than 110 Million infected cases on the globe, sofar. Due to i
The coronavirus disease, named COVID-19, has become the largest global public health crisis since it started in early 2020. CT imaging has been used as a complementary tool to assist early screening, especially for the rapid identification of COVID-1
The capability of generalization to unseen domains is crucial for deep learning models when considering real-world scenarios. However, current available medical image datasets, such as those for COVID-19 CT images, have large variations of infections
With the rapidly worldwide spread of Coronavirus disease (COVID-19), it is of great importance to conduct early diagnosis of COVID-19 and predict the time that patients might convert to the severe stage, for designing effective treatment plan and red
We present an automatic COVID1-19 diagnosis framework from lung CT images. The focus is on signal processing and classification on small datasets with efforts putting into exploring data preparation and augmentation to improve the generalization capa