Longitudinal Self-Supervision for COVID-19 Pathology Quantification

TL;DR

This paper introduces a novel self-supervised learning approach to improve COVID-19 infection quantification from longitudinal CT scans, reducing the need for large labeled datasets and enhancing model performance.

Contribution

The study proposes a new longitudinal self-supervision scheme that effectively trains deep learning models for COVID-19 pathology quantification with limited annotated data.

Findings

Improved accuracy in COVID-19 quantification tasks.

Effective utilization of longitudinal CT scan data.

Enhanced model performance with less labeled data.

Abstract

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.