论文标题
法兰绒：基于局灶性损失的神经网络集合，用于COVID-19检测
FLANNEL: Focal Loss Based Neural Network Ensemble for COVID-19 Detection
论文作者
论文摘要
为了测试使用深层神经网络将COVID-19与其他肺炎和健康患者区分开的可能性。我们从两个公共可用来源构建X射线成像数据，其中包括2874例有四类患者的5508个胸部X射线图像：正常，细菌性肺炎，非舒张性肺炎，19个病毒性肺炎和Covid-19。为了识别COVID-19，我们提出了一个基于局灶性损失的神经集成网络（法兰绒），这是一个灵活的模块，用于集合几个卷积神经网络（CNN）模型，并与局灶性损失融合，以准确地在类不平衡数据上检测到准确的COVID-19。在所有指标中，法兰绒始终优于COVID-19识别任务的基线模型。与最佳基线相比，法兰绒显示出更高的宏观F1得分，而在Covid-19识别任务上相对增加了6％，其精确度达到0.7833（0.07），召回率为0.8609（0.03），0.8168（0.8168）（0.8168（0.03）（0.03）F1得分。
To test the possibility of differentiating chest x-ray images of COVID-19 against other pneumonia and healthy patients using deep neural networks. We construct the X-ray imaging data from two publicly available sources, which include 5508 chest x-ray images across 2874 patients with four classes: normal, bacterial pneumonia, non-COVID-19 viral pneumonia, and COVID-19. To identify COVID-19, we propose a Focal Loss Based Neural Ensemble Network (FLANNEL), a flexible module to ensemble several convolutional neural network (CNN) models and fuse with a focal loss for accurate COVID-19 detection on class imbalance data. FLANNEL consistently outperforms baseline models on COVID-19 identification task in all metrics. Compared with the best baseline, FLANNEL shows a higher macro-F1 score with 6% relative increase on Covid-19 identification task where it achieves 0.7833(0.07) in Precision, 0.8609(0.03) in Recall, and 0.8168(0.03) F1 score.
