Periphery-aware COVID-19 diagnosis with contrastive representation enhancement.
Periphery-aware COVID-19 diagnosis with contrastive representation enhancement.
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DOI：
10.1016/j.patcog.2021.108005
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发表时间：
2021-10
影响因子：
8
通讯作者：
Xue X
中科院分区：
文献类型：
--
作者：
Hou J;Xu J;Jiang L;Du S;Feng R;Zhang Y;Shan F;Xue X
在COVID-19疫情爆发期间，计算机辅助诊断已被广泛研究以进行更快速和准确的筛查。然而，在多类型肺炎分类的复杂情况下区分COVID-19并提高整体诊断性能仍然是一个挑战。在这篇论文中，我们提出了一种新的具有对比表示增强的外周感知COVID-19诊断方法，用于使用胸部CT图像从甲型流感（H1N1）病毒性肺炎、社区获得性肺炎（CAP）和健康受试者中识别COVID-19。我们的主要贡献包括：1）无监督的肺炎感知空间预测（PSP）任务，旨在将重要的空间模式引入深度网络; 2）自适应对比表示增强（CRE）机制，可以有效捕获各种类型肺炎的类内相似性和类间差异。我们整合PSP和CRE以获得在COVID-19筛查中具有高度区分性的表示。我们在我们构建的大规模数据集和两个公共数据集上全面评估了我们的方法。在体积级和切片级CT图像上的大量实验证明了我们提出的PSP和CRE方法用于COVID-19诊断的有效性。
Computer-aided diagnosis has been extensively investigated for more rapid and accurate screening during the outbreak of COVID-19 epidemic. However, the challenge remains to distinguish COVID-19 in the complex scenario of multi-type pneumonia classification and improve the overall diagnostic performance. In this paper, we propose a novel periphery-aware COVID-19 diagnosis approach with contrastive representation enhancement to identify COVID-19 from influenza-A (H1N1) viral pneumonia, community acquired pneumonia (CAP), and healthy subjects using chest CT images. Our key contributions include: 1) an unsupervised Periphery-aware Spatial Prediction (PSP) task which is designed to introduce important spatial patterns into deep networks; 2) an adaptive Contrastive Representation Enhancement (CRE) mechanism which can effectively capture the intra-class similarity and inter-class difference of various types of pneumonia. We integrate PSP and CRE to obtain the representations which are highly discriminative in COVID-19 screening. We evaluate our approach comprehensively on our constructed large-scale dataset and two public datasets. Extensive experiments on both volume-level and slice-level CT images demonstrate the effectiveness of our proposed approach with PSP and CRE for COVID-19 diagnosis.
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深度学习利用 CT 图像准确诊断新型冠状病毒 (COVID-19)
DOI：
10.1109/tcbb.2021.3065361
发表时间：
2021-11
期刊：
IEEE/ACM transactions on computational biology and bioinformatics
影响因子：
--
作者：
Song Y;Zheng S;Li L;Zhang X;Zhang X;Huang Z;Chen J;Wang R;Zhao H;Chong Y;Shen J;Zha Y;Yang Y
通讯作者：
Yang Y
影响因子：
8
作者：
Chen X;Yao L;Zhou T;Dong J;Zhang Y
通讯作者：
Zhang Y
影响因子：
8
作者：
Li J;Zhao G;Tao Y;Zhai P;Chen H;He H;Cai T
通讯作者：
Cai T
影响因子：
5.9
作者：
Wang, Shuai;Kang, Bo;Xu, Bo
通讯作者：
Xu, Bo
影响因子：
8.4
作者：
Amzat, Jimoh;Aminu, Kafayat;Danjibo, Maryann C.
通讯作者：
Danjibo, Maryann C.