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

May 5, 2022, 1:12 a.m. | Constantine Maganaris, Eftychios Protopapadakis, Nikolaos Bakalos, Nikolaos Doulamis, Dimitris Kalogeras, Aikaterini Angeli

cs.LG updates on arXiv.org arxiv.org

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

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