# Longitudinal Self-Supervision for COVID-19 Pathology Quantification

arXiv: https://arxiv.org/abs/2203.10804 · HTML: https://mlsanity.com/paper/2203.10804/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 17 references that are in the map.
- First posted on arXiv 21 Mar 2022.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images](https://mlsanity.com/paper/2004.14133/) (2020)
- [Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis](https://mlsanity.com/paper/1908.06912/) (2019)
- [The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation](https://mlsanity.com/paper/1611.09326/) (2016)
- [Longitudinal Self-Supervised Learning](https://mlsanity.com/paper/2006.06930/) (2020)
- [Spatio-temporal Learning from Longitudinal Data for Multiple Sclerosis Lesion Segmentation](https://mlsanity.com/paper/2004.03675/) (2020)
- [Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs](https://mlsanity.com/paper/2103.07240/) (2021)
- [Interactive Segmentation for COVID-19 Infection Quantification on Longitudinal CT scans](https://mlsanity.com/paper/2110.00948/) (2021)

## Built on by (most cited first, 1 of 1)

- [Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data](https://mlsanity.com/paper/2502.07836/) (2025)

## Abstract (arXiv)

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.