Zhang Hongbin, Liang Weinan, Li Chuanxiu, Xiong Qipeng, Shi Haowei, Hu Lang, Li Guangli
School of Software, East China Jiaotong University, China.
School of Information Engineering, East China Jiaotong University, China.
Biomed Signal Process Control. 2022 Aug;77:103770. doi: 10.1016/j.bspc.2022.103770. Epub 2022 May 2.
COVID-19 is a form of disease triggered by a new strain of coronavirus. Automatic COVID-19 recognition using computer-aided methods is beneficial for speeding up diagnosis efficiency. Current researches usually focus on a deeper or wider neural network for COVID-19 recognition. And the implicit contrastive relationship between different samples has not been fully explored. To address these problems, we propose a novel model, called deep contrastive mutual learning (DCML), to diagnose COVID-19 more effectively. A multi-way data augmentation strategy based on Fast AutoAugment (FAA) was employed to enrich the original training dataset, which helps reduce the risk of overfitting. Then, we incorporated the popular contrastive learning idea into the conventional deep mutual learning (DML) framework to mine the relationship between diverse samples and created more discriminative image features through a new adaptive model fusion method. Experimental results on three public datasets demonstrate that the DCML model outperforms other state-of-the-art baselines. More importantly, DCML is easier to reproduce and relatively efficient, strengthening its high practicality.
新型冠状病毒肺炎（COVID-19）是由一种新型冠状病毒引发的疾病形式。使用计算机辅助方法对COVID-19进行自动识别，有利于加快诊断效率。当前的研究通常聚焦于用于COVID-19识别的更深或更宽的神经网络。并且不同样本之间的隐含对比关系尚未得到充分探索。为了解决这些问题，我们提出了一种名为深度对比互学习（DCML）的新型模型，以更有效地诊断COVID-19。采用了基于快速自动增强（FAA）的多方式数据增强策略来丰富原始训练数据集，这有助于降低过拟合风险。然后，我们将流行的对比学习思想融入传统的深度互学习（DML）框架，以挖掘不同样本之间的关系，并通过一种新的自适应模型融合方法创建更具判别力的图像特征。在三个公共数据集上的实验结果表明，DCML模型优于其他现有最先进的基线模型。更重要的是，DCML更易于重现且相对高效，增强了其高实用性。
