  Quantifying COVID-19 infection over time is an important task to manage the
hospitalization of patients during a global pandemic. Recently, deep
learning-based approaches have been proposed to help radiologists automatically
quantify COVID-19 pathologies on longitudinal CT scans. However, the learning
process of deep learning methods demands extensive training data to learn the
complex characteristics of infected regions over longitudinal scans. It is
challenging to collect a large-scale dataset, especially for longitudinal
training. In this study, we want to address this problem by proposing a new
self-supervised learning method to effectively train longitudinal networks for
the quantification of COVID-19 infections. For this purpose, longitudinal
self-supervision schemes are explored on clinical longitudinal COVID-19 CT
scans. Experimental results show that the proposed method is effective, helping
the model better exploit the semantics of longitudinal data and improve two
COVID-19 quantification tasks.
