Role of standard and soft tissue chest radiography images in deep-learning-based early diagnosis of COVID-19.
Role of standard and soft tissue chest radiography images in deep-learning-based early diagnosis of COVID-19.
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DOI：
10.1117/1.jmi.8.s1.014503
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发表时间：
2021-01
期刊：
影响因子：
--
通讯作者：
Giger ML
中科院分区：
文献类型：
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作者：
Hu Q;Drukker K;Giger ML
目的：我们提出了一种深度学习方法，用于在胸部 X 线摄影 (CXR) 图像上的患者呈现时自动诊断 COVID-19，并研究标准和软组织 CXR 在此任务中的作用。方法：该数据集包含 9860 名患者在首次进行 SARS-CoV-2 病毒逆转录聚合酶链反应检测后 2 天内进行的首次 CXR 检查，其中 1523 名患者 (15.5%) 检测结果呈阳性，其中 8337 名患者 (84.5%) 检测结果呈阴性。采用顺序迁移学习策略来针对日益具体和复杂的任务分阶段微调卷积神经网络。 COVID-19 阳性/阴性分类是对标准图像、软组织图像以及两者通过特征融合组合进行的。在执行分类之前，使用 U-Net 变体对每张图像中的肺部区域进行分割和裁剪。使用受试者工作特征曲线下面积 (AUC) 和 DeLong 检验在 1972 名患者的保留测试集上评估和比较分类性能。结果：使用全标准、裁剪标准、裁剪、软组织和两种类型的裁剪 CXR 产生的 AUC 值分别为 0.74 [0.70, 0.77]、0.76 [0.73, 0.79]、0.73 [0.70, 0.76] 和 0.78 [0.74, 0.81]。使用软组织图像的性能明显低于标准图像，并且使用两种类型的 CXR 未能显着优于单独使用标准图像。结论：所提出的方法能够在患者就诊时自动诊断 COVID-19，具有良好的性能，并且包含软组织图像并没有带来显着的性能改进。
Purpose: We propose a deep learning method for the automatic diagnosis of COVID-19 at patient presentation on chest radiography (CXR) images and investigates the role of standard and soft tissue CXR in this task. Approach: The dataset consisted of the first CXR exams of 9860 patients acquired within 2 days after their initial reverse transcription polymerase chain reaction tests for the SARS-CoV-2 virus, 1523 (15.5%) of whom tested positive and 8337 (84.5%) of whom tested negative for COVID-19. A sequential transfer learning strategy was employed to fine-tune a convolutional neural network in phases on increasingly specific and complex tasks. The COVID-19 positive/negative classification was performed on standard images, soft tissue images, and both combined via feature fusion. A U-Net variant was used to segment and crop the lung region from each image prior to performing classification. Classification performances were evaluated and compared on a held-out test set of 1972 patients using the area under the receiver operating characteristic curve (AUC) and the DeLong test. Results: Using full standard, cropped standard, cropped, soft tissue, and both types of cropped CXR yielded AUC values of 0.74 [0.70, 0.77], 0.76 [0.73, 0.79], 0.73 [0.70, 0.76], and 0.78 [0.74, 0.81], respectively. Using soft tissue images significantly underperformed standard images, and using both types of CXR failed to significantly outperform using standard images alone. Conclusions: The proposed method was able to automatically diagnose COVID-19 at patient presentation with promising performance, and the inclusion of soft tissue images did not result in a significant performance improvement.