The recent Coronavirus Disease 2019 (COVID-19) pandemic has put a tremendous burden on global health systems. Medical practitioners are under great pressure for reliable screening of suspected cases employing adjunct diagnostic tools to standard point-of-care testing methodology. Chest X-rays (CXRs) are appearing as a prospective diagnostic tool with easy-to-acquire, low-cost and less cross-contamination risk features. Artificial intelligence (AI)-attributed CXR evaluation has shown great potential for distinguishing COVID-19-induced pneumonia from other associated clinical instances. However, one of the associated challenges with diagnostic imaging-based modeling is incorrect feature attribution, which leads the model to learn misguiding disease patterns, causing wrong predictions. Here, we demonstrate an effective deep learning-based methodology to mitigate the problem, thereby allowing the classification algorithm to learn from relevant features. The proposed deep-learning framework consists of an ensemble of convolutional neural network (CNN) models focusing on both global and local pathological features from CXR lung images, while the latter is extracted using a multi-instance learning scheme and a local attention mechanism. An inspection of a series of backbone CNN models using global and local features, and an ensemble of both features, trained from high-quality CXR images of 1311 patients, further augmented for achieving the symmetry in class distribution, to localize lung pathological features followed by the classification of COVID-19 and other related pneumonia, shows that a DenseNet161 architecture outperforms all other models, as evaluated on an independent test set of 159 patients with confirmed cases. Specifically, an ensemble of DenseNet161 models with global and local attention-based features achieve an average balanced accuracy of 91.2%, average precision of 92.4%, and F1-score of 91.9% in a multi-label classification framework comprising COVID-19, pneumonia, and control classes. The DenseNet161 ensembles were also found to be statistically significant from all other models in a comprehensive statistical analysis. The current study demonstrated that the proposed deep learning-based algorithm can accurately identify the COVID-19-related pneumonia in CXR images, along with differentiating non-COVID-19-associated pneumonia with high specificity, by effectively alleviating the incorrect feature attribution problem, and exploiting an enhanced feature descriptor.

中文翻译：

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基于全局和局部注意力的卷积神经网络的组合，用于胸部X射线图像的COVID-19诊断

最近的2019年冠状病毒病（COVID-19）大流行给全球卫生系统带来了沉重负担。从业人员承受着巨大压力，需要使用辅助诊断工具和标准即时检验方法对可疑病例进行可靠筛查。胸部X光（CXR）似乎是一种前瞻性的诊断工具，具有易于获取，成本低和交叉污染风险小等特点。人工智能（AI）进行的CXR评估显示出将COVID-19诱发的肺炎与其他相关临床病例区分开来的巨大潜力。但是，基于诊断成像的建模的相关挑战之一是特征归因不正确，这会导致模型学习错误的疾病模式，从而导致错误的预测。这里，我们展示了一种有效的基于深度学习的方法来缓解该问题，从而使分类算法可以从相关特征中学习。拟议的深度学习框架包括一个集中于CXR肺图像的全局和局部病理特征的卷积神经网络（CNN）模型集合，而后者是使用多实例学习方案和局部注意机制提取的。使用1311名患者的高质量CXR图像对使用全局和局部特征的一系列主干CNN模型进行检查，并结合了这两种特征，进一步增强功能以​​实现班级分布的对称性，从而定位肺部病理特征，随后进行COVID-19和其他相关性肺炎的分类，结果表明，根据对159例确诊病例的独立测试集进行的评估，DenseNet161体系结构的性能优于所有其他模型。具体来说，在具有COVID-19，肺炎的多标签分类框架中，具有全局和局部基于关注特征的DenseNet161模型的集成可实现91.2％的平均平衡准确度，92.4％的平均精确度和91.9％的F1得分。和控件类。在全面的统计分析中，与所有其他模型相比，DenseNet161合奏也具有统计学意义。当前的研究表明，基于深度学习的算法可以准确识别CXR图像中的COVID-19相关性肺炎，并且可以高度区分非COVID-19相关性肺炎，