Coronavirus Disease 2019 (COVID-19) has rapidly spread worldwide since first reported. Timely diagnosis of COVID-19 is crucial both for disease control and patient care. Non-contrast thoracic computed tomography (CT) has been identified as an effective tool for the diagnosis, yet the disease outbreak has placed tremendous pressure on radiologists for reading the exams and may potentially lead to fatigue-related mis-diagnosis. Reliable automatic classification algorithms can be really helpful; however, they usually require a considerable number of COVID-19 cases for training, which is difficult to acquire in a timely manner. Meanwhile, how to effectively utilize the existing archive of non-COVID-19 data (the negative samples) in the presence of severe class imbalance is another challenge. In addition, the sudden disease outbreak necessitates fast algorithm development. In this work, we propose a novel approach for effective and efficient training of COVID-19 classification networks using a small number of COVID-19 CT exams and an archive of negative samples. Concretely, a novel self-supervised learning method is proposed to extract features from the COVID-19 and negative samples. Then, two kinds of soft-labels (‘difficulty’ and ‘diversity’) are generated for the negative samples by computing the earth mover's distances between the features of the negative and COVID-19 samples, from which data ‘values’ of the negative samples can be assessed. A pre-set number of negative samples are selected accordingly and fed to the neural network for training. Experimental results show that our approach can achieve superior performance using about half of the negative samples, substantially reducing model training time.

中文翻译：

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通过自监督双轨学习排名，高效且有效地训练 COVID-19 分类网络。

自首次报道以来，2019 年冠状病毒病 (COVID-19) 已在全球范围内迅速传播。及时诊断 COVID-19 对于疾病控制和患者护理至关重要。非增强胸部计算机断层扫描（CT）已被认为是一种有效的诊断工具，但疾病的爆发给放射科医生阅读检查结果带来了巨大的压力，并可能导致与疲劳相关的误诊。可靠的自动分类算法确实很有帮助；然而，他们通常需要大量的COVID-19病例进行培训，而这些病例很难及时获得。同时，在类别严重失衡的情况下，如何有效利用现有的非COVID-19数据（负样本）档案是另一个挑战。此外，突发的疾病爆发需要快速的算法开发。在这项工作中，我们提出了一种新方法，使用少量的 COVID-19 CT 检查和阴性样本档案来有效且高效地训练 COVID-19 分类网络。具体来说，提出了一种新颖的自监督学习方法来从 COVID-19 和负样本中提取特征。然后，通过计算负样本特征与 COVID-19 样本特征之间的推土机距离，为负样本生成两种软标签（“难度”和“多样性”），从中得出负样本的数据“值”可以评估样品。相应地选择预设数量的负样本并将其馈送到神经网络进行训练。实验结果表明，我们的方法可以使用大约一半的负样本实现优异的性能，从而大大减少模型训练时间。