Momentum contrastive learning for few-shot COVID-19 diagnosis from chest CT images.
Momentum contrastive learning for few-shot COVID-19 diagnosis from chest CT images.
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DOI：
10.1016/j.patcog.2021.107826
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发表时间：
2021-05
影响因子：
8
通讯作者：
Zhang Y
中科院分区：
文献类型：
--
作者：
Chen X;Yao L;Zhou T;Dong J;Zhang Y
目前的疫情是由2019年12月新型冠状病毒(新冠肺炎)爆发引起的，已导致全球紧急情况，对世界各地的经济、医疗体系和个人福祉产生了重大影响。控制这种快速演变的疾病需要高度敏感和特定的诊断。虽然RT-PCR是最常用的，但它可能需要长达8小时的时间，并且需要医疗保健专业人员的大量努力。因此，迫切需要一种快速、自动的诊断系统。从胸部CT图像进行诊断是一个很有前途的方向。然而，目前的研究由于缺乏足够的训练样本而受到限制，因为获取带注释的CT图像是很耗时的。为此，我们提出了一种新的深度学习算法用于新冠肺炎的自动诊断，该算法只需要几个样本进行训练。具体地说，我们使用对比学习来训练一个编码器，该编码器可以捕获大量公开可用的肺部数据集上的表达特征，并采用原型网络进行分类。我们在两个公开可用的带注释的新冠肺炎CT数据集上与其他竞争方法进行了比较，验证了所提出的模型的有效性。我们的结果表明，我们的模型对于基于胸部CT图像的新冠肺炎的准确诊断具有优越的性能。
The current pandemic, caused by the outbreak of a novel coronavirus (COVID-19) in December 2019, has led to a global emergency that has significantly impacted economies, healthcare systems and personal wellbeing all around the world. Controlling the rapidly evolving disease requires highly sensitive and specific diagnostics. While RT-PCR is the most commonly used, it can take up to eight hours, and requires significant effort from healthcare professionals. As such, there is a critical need for a quick and automatic diagnostic system. Diagnosis from chest CT images is a promising direction. However, current studies are limited by the lack of sufficient training samples, as acquiring annotated CT images is time-consuming. To this end, we propose a new deep learning algorithm for the automated diagnosis of COVID-19, which only requires a few samples for training. Specifically, we use contrastive learning to train an encoder which can capture expressive feature representations on large and publicly available lung datasets and adopt the prototypical network for classification. We validate the efficacy of the proposed model in comparison with other competing methods on two publicly available and annotated COVID-19 CT datasets. Our results demonstrate the superior performance of our model for the accurate diagnosis of COVID-19 based on chest CT images.
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DOI：
10.1007/s10044-021-00984-y
发表时间：
2021
期刊：
Pattern analysis and applications : PAA
影响因子：
--
作者：
Narin A;Kaya C;Pamuk Z
通讯作者：
Pamuk Z
影响因子：
168.9
作者：
Huang, Chaolin;Wang, Yeming;Cao, Bin
通讯作者：
Cao, Bin
影响因子：
4.6
作者：
Chen J;Wu L;Zhang J;Zhang L;Gong D;Zhao Y;Chen Q;Huang S;Yang M;Yang X;Hu S;Wang Y;Hu X;Zheng B;Zhang K;Wu H;Dong Z;Xu Y;Zhu Y;Chen X;Zhang M;Yu L;Cheng F;Yu H
通讯作者：
Yu H
影响因子：
19.7
作者：
Li, Lin;Qin, Lixin;Xia, Jun
通讯作者：
Xia, Jun
影响因子：
82.9
作者：
Mei, Xueyan;Lee, Hao-Chih;Yang, Yang
通讯作者：
Yang, Yang