论文标题
COVID-19基于胸部X射线图像的自我监督转移学习的检测
COVID-19 Detection Based on Self-Supervised Transfer Learning Using Chest X-Ray Images
论文作者
论文摘要
目的：考虑到由于19日大流行而被筛查的几名患者，计算机辅助检测在协助临床工作流程效率和降低放射科医生和医疗保健提供者中感染的发生率方面具有强大的潜力。由于许多确认的COVID 19例表现出肺炎的放射学发现，因此放射学检查可用于快速检测。因此，胸部X射线照相可用于快速筛选患者分类期间的Covid-19，从而确定患者护理的优先级，以帮助在大流行状况下帮助饱和的医疗设施。方法：在本文中，我们提出了一种新的学习方案，称为自我监督的转移学习，用于从胸部X射线（CXR）图像检测COVID-19。我们将六种自我监督的学习（SSL）方法（交叉，BYOL，SIMSIAM，SIMCLR，PIRL-JIGSAW和PIRL ROTATION）与所提出的方法进行了比较。此外，我们将六个验证的DCNN（RESNET18，RESNET50，RESNET101，CHEXNET，DENSENET201和INCEPTIONV3）与建议的方法进行了比较。我们对最大的开放Covid-19 CXR数据集和视觉检查定性结果提供定量评估。结果：我们的方法达到的谐波平均值（HM）得分为0.985，AUC为0.999，四级精度为0.953。我们还使用可视化技术Grad-CAM ++用建议的方法来生成不同类别的CXR图像的视觉解释，以提高可解释性。结论：我们的方法表明，使用传递学习从自然图像中学到的知识对CXR图像的SSL有益，并提高了COVID-19检测的代表性学习的性能。我们的方法有望减少放射科医生和医疗保健提供者中感染的发生率。
Purpose: Considering several patients screened due to COVID-19 pandemic, computer-aided detection has strong potential in assisting clinical workflow efficiency and reducing the incidence of infections among radiologists and healthcare providers. Since many confirmed COVID-19 cases present radiological findings of pneumonia, radiologic examinations can be useful for fast detection. Therefore, chest radiography can be used to fast screen COVID-19 during the patient triage, thereby determining the priority of patient's care to help saturated medical facilities in a pandemic situation. Methods: In this paper, we propose a new learning scheme called self-supervised transfer learning for detecting COVID-19 from chest X-ray (CXR) images. We compared six self-supervised learning (SSL) methods (Cross, BYOL, SimSiam, SimCLR, PIRL-jigsaw, and PIRL-rotation) with the proposed method. Additionally, we compared six pretrained DCNNs (ResNet18, ResNet50, ResNet101, CheXNet, DenseNet201, and InceptionV3) with the proposed method. We provide quantitative evaluation on the largest open COVID-19 CXR dataset and qualitative results for visual inspection. Results: Our method achieved a harmonic mean (HM) score of 0.985, AUC of 0.999, and four-class accuracy of 0.953. We also used the visualization technique Grad-CAM++ to generate visual explanations of different classes of CXR images with the proposed method to increase the interpretability. Conclusions: Our method shows that the knowledge learned from natural images using transfer learning is beneficial for SSL of the CXR images and boosts the performance of representation learning for COVID-19 detection. Our method promises to reduce the incidence of infections among radiologists and healthcare providers.
