Various AI functionalities such as pattern recognition and prediction can effectively be used to diagnose (recognize) and predict coronavirus disease 2019 (COVID-19) infections and propose timely response (remedial action) to minimize the spread and impact of the virus. Motivated by this, an AI system based on deep meta learning has been proposed in this research to accelerate analysis of chest X-ray (CXR) images in automatic detection of COVID-19 cases. We present a synergistic approach to integrate contrastive learning with a fine-tuned pre-trained ConvNet encoder to capture unbiased feature representations and leverage a Siamese network for final classification of COVID-19 cases. We validate the effectiveness of our proposed model using two publicly available datasets comprising images from normal, COVID-19 and other pneumonia infected categories. Our model achieves 95.6% accuracy and AUC of 0.97 in diagnosing COVID-19 from CXR images even with a limited number of training samples.

中文翻译：

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MetaCOVID：一种具有对比损失的连体神经网络框架，用于 COVID-19 患者的 n-shot 诊断

模式识别和预测等各种人工智能功能可有效用于诊断（识别）和预测 2019 年冠状病毒病（COVID-19）感染，并提出及时响应（补救措施）以最大限度地减少病毒的传播和影响。受此启发，本研究提出了一种基于深度元学习的人工智能系统，以在自动检测 COVID-19 病例时加速胸部 X 光 (CXR) 图像的分析。我们提出了一种协同方法，将对比学习与经过微调的预训练 ConvNet 编码器相结合，以捕获无偏差的特征表示，并利用 Siamese 网络对 COVID-19 病例进行最终分类。我们使用两个公开可用的数据集验证了我们提出的模型的有效性，这些数据集包含来自正常、COVID-19 和其他肺炎感染类别。即使训练样本数量有限，我们的模型在从 CXR 图像诊断 COVID-19 时也达到了 95.6% 的准确率和 0.97 的 AUC。