论文标题
使用CT SARS-COV-2分割模型评估COVID 3D定位的可传递性
Evaluating Transferability for Covid 3D Localization Using CT SARS-CoV-2 segmentation models
论文作者
论文摘要
最近的研究表明，在CT扫描上检测射线照相模式可以产生高灵敏度和特异性，以使其对COVID-19的定位。在本文中，我们研究了深度学习模型可传递性的适当性，用于对CT图像中肺炎感染区域的语义分割。转移学习允许对检测模型进行快速初始化/重新化，鉴于没有大量培训数据。我们的工作探讨了在特定的CT数据集上使用预训练的U-NET体系结构的功效，以识别来自不同数据集的图像上的COVID-19副作用。实验结果表明，鉴定COVID-19受感染区域的分割准确性的提高。
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.
