# Evaluating Transferability for Covid 3D Localization Using CT SARS-CoV-2 segmentation models

arXiv: https://arxiv.org/abs/2205.02152 · HTML: https://mlsanity.com/paper/2205.02152/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 28 references that are in the map.
- First posted on arXiv 4 May 2022.

## Builds on (most cited first, 1 of 1)

- [Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images](https://mlsanity.com/paper/2004.14133/) (2020)

## Built on by (most cited first, 2 of 2)

- [Transferability limitations for Covid 3D Localization Using SARS-CoV-2 segmentation models in 4D CT images](https://mlsanity.com/paper/2208.08343/) (2022)
- [Enhancing Deep Learning Model Explainability in Brain Tumor Datasets using Post-Heuristic Approaches](https://mlsanity.com/paper/2404.19568/) (2024)

## Abstract (arXiv)

Recent studies indicate that detecting radiographic patterns on CT scans can yield high sensitivity and specificity for Covid-19 localization. In this paper, we investigate the appropriateness of deep learning models transferability, for semantic segmentation of pneumonia-infected areas in CT images. Transfer learning allows for the fast initialization/reutilization of detection models, given that large volumes of training data are not available. Our work explores the efficacy of using pre-trained U-Net architectures, on a specific CT data set, for identifying Covid-19 side-effects over images from different datasets. Experimental results indicate improvement in the segmentation accuracy of identifying Covid-19 infected regions.