Coronavirus disease 2019 (COVID-19) is a Public Health Emergency of International Concern infecting more than 40 million people across 188 countries and territories. Chest computed tomography (CT) imaging technique benefits from its high diagnostic accuracy and robustness, it has become an indispensable way for COVID-19 mass testing. Recently, deep learning approaches have become an effective tool for automatic screening of medical images, and it is also being considered for COVID-19 diagnosis. However, the high infection risk involved with COVID-19 leads to relative sparseness of collected labeled data limiting the performance of such methodologies. Moreover, accurately labeling CT images require expertise of radiologists making the process expensive and time-consuming. In order to tackle the above issues, we propose a supervised domain adaption based COVID-19 CT diagnostic method which can perform effectively when only a small samples of labeled CT scans are available. To compensate for the sparseness of labeled data, the proposed method utilizes a large amount of synthetic COVID-19 CT images and adjusts the networks from the source domain (synthetic data) to the target domain (real data) with a cross-domain training mechanism. Experimental results show that the proposed method achieves state-of-the-art performance on few-shot COVID-19 CT imaging based diagnostic tasks.
翻译:2019年科罗纳病毒疾病(COVID-19)是国际关注关注的公共卫生紧急情况,在188个国家和地区有4 000多万人感染了2019年科罗纳病毒(COVID-19),胸前计算断层成像技术因其诊断精度和稳健性高而受益,已成为COVID-19大规模测试不可或缺的途径。最近,深层学习方法已成为自动筛选医疗图像的有效工具,也正在考虑进行COVID-19诊断。但是,COVID-19的高感染风险导致收集的贴标签数据相对稀少,限制了这种方法的性能。此外,准确标出CT图像的标签需要放射学家的专门知识,使这一过程变得昂贵和耗时。为了解决上述问题,我们提出了一种基于COVID-19的受监督域调整方法。当只有少量贴标签的CT扫描样本时,才能有效发挥作用。为了补偿标签数据稀少,拟议的方法使用大量合成COVID-19CT图像合成数据,并从源域域(合成数据)调整网络,需要辐射学家的专门知识,使这一过程变得昂贵和耗时耗时费。为了解决上述问题,我们建议提出的领域,我们提出了一种以实验性成像化方法,从而显示基于实验域的成果。