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利用深度学习系统筛查新冠病毒肺炎
Xiaowei Xu, Xiangao Jiang, Chunlian Ma, Peng Du, Xukun Li, Shuangzhi Lv, Liang Yu, Qin Ni, Yanfei Chen, Junwei Su, Guanjing Lang, Yongtao Li, Hong Zhao, Jun Liu, Kaijin Xu, Lingxiang Ruan, Jifang Sheng, Yunqing Qiu, Wei Wu, Tingbo Liang, Lanjuan Li
工程（英文） ›› 2020, Vol. 6 ›› Issue (10) : 1122-1129.
PDF(1413 KB)
PDF(1413 KB)
利用深度学习系统筛查新冠病毒肺炎
A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia
实时逆转录聚合酶链反应（RT-PCR）检测早期新冠病毒肺炎（COVID-19）患者的痰液或鼻咽拭子中的病毒RNA阳性率较低。同时，COVID-19的计算机断层扫描（CT）影像学的临床表现有其自身的特点，不同于甲型流感病毒性肺炎（IAVP）等其他类型的病毒性肺炎。本研究旨在应用深度学习技术，建立COVID-19、IAVP及健康人群肺部CT的早期筛查模型。本研究共采集618份CT样本，其中219份样本来自110例COVID-19患者（平均年龄50岁，其中男性63例，占57.3%），224份样本来自224例IAVP患者（平均年龄61岁，其中男性156例，占69.6%），175份样本来自健康人群（平均年龄39岁，其中男性97例，占55.4%）。所有CT样本均来自浙江省三家COVID-19定点收治医院。我们首先利用胸部CT图像集的三维（3D）深度学习模型分割出候选感染区域，然后利用位置敏感机制深度学习网络将这些分离的图像归类为COVID-19、IAVP以及与感染无关（ITI）的图像，并且输出相应置信度得分。最后，用Noisy-OR贝叶斯函数计算每份CT病例的感染类型及总置信度。测试数据集的实验结果表明，从整体CT病例来看，本研究利用深度学习系统建立的COVID-19患者的早期筛查模型的总体准确率为86.7%。该模型有望成为一线临床医生诊断COVID-19的一种有效的辅助方法。
The real-time reverse transcription-polymerase chain reaction (RT-PCR) detection of viral RNA from sputum or nasopharyngeal swab had a relatively low positive rate in the early stage of coronavirus disease 2019 (COVID-19). Meanwhile, the manifestations of COVID-19 as seen through computed tomography (CT) imaging show individual characteristics that differ from those of other types of viral pneumonia such as influenza-A viral pneumonia (IAVP). This study aimed to establish an early screening model to distinguish COVID-19 pneumonia from IAVP and healthy cases through pulmonary CT images using deep learning techniques. A total of 618 CT samples were collected: 219 samples from 110 patients with COVID-19 (mean age 50 years; 63 (57.3%) male patients); 224 samples from 224 patients with IAVP (mean age 61 years; 156 (69.6%) male patients); and 175 samples from 175 healthy cases (mean age 39 years; 97 (55.4%) male patients). All CT samples were contributed from three COVID-19-designated hospitals in Zhejiang Province, China. First, the candidate infection regions were segmented out from the pulmonary CT image set using a 3D deep learning model. These separated images were then categorized into the COVID-19, IAVP, and irrelevant to infection (ITI) groups, together with the corresponding confidence scores, using a location-attention classification model. Finally, the infection type and overall confidence score for each CT case were calculated using the Noisy-OR Bayesian function. The experimental result of the benchmark dataset showed that the overall accuracy rate was 86.7% in terms of all the CT cases taken together. The deep learning models established in this study were effective for the early screening of COVID-19 patients and were demonstrated to be a promising supplementary diagnostic method for frontline clinical doctors.
COVID-19 / 位置敏感机制深度学习网络 / 计算机断层扫描 /
Coronavirus disease 2019 pneumonia / COVID-19 / Location-attention classification model / Computed tomography
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