Evaluating Transferability for Covid 3D Localization Using CT SARS-CoV-2 segmentation models. (arXiv:2205.02152v1 [eess.IV])

Web: http://arxiv.org/abs/2205.02152

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 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 …

3d arxiv covid localization models segmentation