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.
翻译:最近的研究表明,检测CT扫描的放射线学模式可以为Covid-19本地化带来高度的敏感性和特殊性。在本文件中,我们调查了深学习模型的可转移性是否适宜,用于CT图像中肺炎感染地区的语系分解。传输学习使检测模型的快速初始化/再利用成为可能,因为没有大量的培训数据。我们的工作探索了在特定CT数据集上使用预先培训的U-Net结构的效率,以确定Covid-19对不同数据集图像的副作用。实验结果表明,识别Covid-19受感染地区的分解准确性有所提高。