Hou Junlin, Xu Jilan, Jiang Longquan, Du Shanshan, Feng Rui, Zhang Yuejie, Shan Fei, Xue Xiangyang
School of Computer Science, Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China.
School of Information Science and Technology, Fudan University, Shanghai, China.
Pattern Recognit. 2021 Oct;118:108005. doi: 10.1016/j.patcog.2021.108005. Epub 2021 May 6.
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.
在新冠疫情爆发期间，计算机辅助诊断已被广泛研究，以实现更快速、准确的筛查。然而，在多类型肺炎分类的复杂场景中区分新冠肺炎，并提高整体诊断性能仍然是一个挑战。在本文中，我们提出了一种新颖的具有对比表示增强的周边感知新冠肺炎诊断方法，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新冠肺炎。我们的主要贡献包括：1）一个无监督的周边感知空间预测（PSP）任务，旨在将重要的空间模式引入深度网络；2）一种自适应对比表示增强（CRE）机制，它可以有效地捕捉各类肺炎的类内相似性和类间差异。我们将PSP和CRE集成起来，以获得在新冠肺炎筛查中具有高度判别力的表示。我们在构建的大规模数据集和两个公共数据集上对我们的方法进行了全面评估。在体积级和切片级CT图像上进行的大量实验证明了我们提出的带有PSP和CRE的方法在新冠肺炎诊断中的有效性。
