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.
@article{arxiv.2203.10804,
title = {Longitudinal Self-Supervision for COVID-19 Pathology Quantification},
author = {Tobias Czempiel and Coco Rogers and Matthias Keicher and Magdalini Paschali and Rickmer Braren and Egon Burian and Marcus Makowski and Nassir Navab and Thomas Wendler and Seong Tae Kim},
journal= {arXiv preprint arXiv:2203.10804},
year = {2022}
}