# COVID-19 in CXR: from Detection and Severity Scoring to Patient Disease Monitoring

arXiv: https://arxiv.org/abs/2008.02150 · HTML: https://mlsanity.com/paper/2008.02150/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Aug 2020.

## Built on by (most cited first, 5 of 5)

- [CXR Segmentation by AdaIN-based Domain Adaptation and Knowledge Distillation](https://mlsanity.com/paper/2104.05892/) (2021)
- [COVID-19 Pneumonia Severity Prediction using Hybrid Convolution-Attention Neural Architectures](https://mlsanity.com/paper/2107.02672/) (2021)
- [Vision Transformer-based Model for Severity Quantification of Lung Pneumonia Using Chest X-ray Images](https://mlsanity.com/paper/2303.11935/) (2023)
- [Exploring the Capabilities of Large Language Model Encoders for Image-Text Retrieval in Chest X-rays](https://mlsanity.com/paper/2509.15234/) (2025)
- [Tiled sparse coding in eigenspaces for the COVID-19 diagnosis in chest X-ray images](https://mlsanity.com/paper/2106.14724/) (2021)

## Abstract (arXiv)

In this work, we estimate the severity of pneumonia in COVID-19 patients and conduct a longitudinal study of disease progression. To achieve this goal, we developed a deep learning model for simultaneous detection and segmentation of pneumonia in chest Xray (CXR) images and generalized to COVID-19 pneumonia. The segmentations were utilized to calculate a "Pneumonia Ratio" which indicates the disease severity. The measurement of disease severity enables to build a disease extent profile over time for hospitalized patients. To validate the model relevance to the patient monitoring task, we developed a validation strategy which involves a synthesis of Digital Reconstructed Radiographs (DRRs - synthetic Xray) from serial CT scans; we then compared the disease progression profiles that were generated from the DRRs to those that were generated from CT volumes.