论文标题
肺部分割对胸部X射线图像中COVID-19的诊断和解释的影响
Impact of lung segmentation on the diagnosis and explanation of COVID-19 in chest X-ray images
论文作者
论文摘要
Covid-19经常引起肺炎，可以使用成像检查诊断。胸部X射线（CXR）通常很有用，因为它便宜，快速，广泛，并且使用较少的辐射。在这里，我们证明了使用CXR图像在COVID-19鉴定中肺部分割的影响，并评估该图像的哪些内容影响最大。使用U-NET CNN体系结构进行语义分割，并使用三个CNN体系结构（VGG，Resnet和Inception）进行分类。采用了可解释的人工智能技术来估计分割的影响。组成了三类数据库：肺不透明度（肺炎），covid-19和正常。我们评估了从不同来源创建CXR图像数据库的影响，以及从一个来源到另一个来源的COVID-19概括。分割的JACCARD距离为0.034，骰子系数为0.982。使用分段图像的分类为多级设置达到0.88的F1得分，对于COVID-19识别率为0.83。在跨数据库方案中，我们获得了使用分段图像的COVID-19识别的ROC曲线下的F1得分为0.74，ROC曲线下的面积为0.9。实验支持以下结论：即使在细分后，来自不同来源的潜在因素也会引起强烈的偏见。
COVID-19 frequently provokes pneumonia, which can be diagnosed using imaging exams. Chest X-ray (CXR) is often useful because it is cheap, fast, widespread, and uses less radiation. Here, we demonstrate the impact of lung segmentation in COVID-19 identification using CXR images and evaluate which contents of the image influenced the most. Semantic segmentation was performed using a U-Net CNN architecture, and the classification using three CNN architectures (VGG, ResNet, and Inception). Explainable Artificial Intelligence techniques were employed to estimate the impact of segmentation. A three-classes database was composed: lung opacity (pneumonia), COVID-19, and normal. We assessed the impact of creating a CXR image database from different sources, and the COVID-19 generalization from one source to another. The segmentation achieved a Jaccard distance of 0.034 and a Dice coefficient of 0.982. The classification using segmented images achieved an F1-Score of 0.88 for the multi-class setup, and 0.83 for COVID-19 identification. In the cross-dataset scenario, we obtained an F1-Score of 0.74 and an area under the ROC curve of 0.9 for COVID-19 identification using segmented images. Experiments support the conclusion that even after segmentation, there is a strong bias introduced by underlying factors from different sources.
