Evaluating Transferability for Covid 3D Localization Using CT SARS-CoV-2 segmentation models
This work addresses the need for efficient Covid-19 localization in medical imaging when training data is limited, but it is incremental as it applies existing methods to new data.
The paper tackled the problem of segmenting Covid-19 infected regions in CT scans by evaluating the transferability of pre-trained U-Net models across different datasets, resulting in improved segmentation accuracy.
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.