杜臻宇,帕力旦·吐尔逊,范迎迎,等.像素分割联合深度双分支模型辅助诊断新冠CT图像[J]. 微电子学与计算机,2023,40(6):42-50. doi: 10.19304/J.ISSN1000-7180.2022.0374
引用本文: 杜臻宇,帕力旦·吐尔逊,范迎迎,等.像素分割联合深度双分支模型辅助诊断新冠CT图像[J]. 微电子学与计算机,2023,40(6):42-50. doi: 10.19304/J.ISSN1000-7180.2022.0374
DU Z Y,Palladium Turson,FAN Y Y,et al. Pixel segmentation combined with a depth dual-branch model assists in the diagnosis of COVID-19 CT images[J]. Microelectronics & Computer,2023,40(6):42-50. doi: 10.19304/J.ISSN1000-7180.2022.0374
Citation: DU Z Y,Palladium Turson,FAN Y Y,et al. Pixel segmentation combined with a depth dual-branch model assists in the diagnosis of COVID-19 CT images[J]. Microelectronics & Computer,2023,40(6):42-50. doi: 10.19304/J.ISSN1000-7180.2022.0374

像素分割联合深度双分支模型辅助诊断新冠CT图像

Pixel segmentation combined with a depth dual-branch model assists in the diagnosis of COVID-19 CT images

  • 摘要: 基于医学肺部影像开发智能诊断新冠肺炎的深度学习方法能够减轻大量医护人员的工作,且能够提供可靠的准确性,然而深度学习方法的高准确性通常依赖于数据样本的质量. 在自然界存在的医学图像数据的来源和处理过程并不单一,数据样本差异性较大和质量不佳会增大深度学习模型提取关键特征的难度,有效的数据预处理和合适的模型设计十分关键. 基于肺部CT图像,本论文提出一种像素分割联合双分支模型ReSWNet辅助诊断新冠肺炎感染. 该方法首先训练像素分割模型进行分割预处理,实现肺部CT图像无关背景的剔除,然后通过结合了卷积神经网络和自注意力模型优缺点的双分支模型进行肺炎诊断. 通过在COVID-CT数据集上对该方法进行验证表明,在诊断准确率、召回率和F1分数等性能指标方面,该方法较基线模型分别提高了8.6%、16.05以及7.71%,最后采用可视化结果热力图为诊断提供了可解释性.

     

    Abstract: Developing deep learning methods for intelligently diagnosing COVID-19 based on medical lung imaging can ease the work of a large number of healthcare workers and provide reliable accuracy, but the high accuracy of deep learning methods often depends on the quality of data samples. The source and processing process of medical image data that exists in nature is not single, and the large difference and poor quality of data samples will increase the difficulty of deep learning models to extract key features, and effective data pre-processing and appropriate model design are critical. Based on lung CT images, this paper proposes a pixel-segmentation combined dual-branching model ReSWNet to assist in the diagnosis of COVID-19 infection. This method first trains the pixel segmentation model for segmentation preprocessing to achieve the rejection of the irrelevant background of lung CT images, and then performs pneumonia diagnosis by combining the advantages and disadvantages of convolutional neural network and self-attention model. The method was validated on the COVID-CT dataset and showed that the method improved by 8.6%, 16.05 and 7.71% compared with the baseline model in terms of diagnostic accuracy, recall rate and F1 score, respectively, and finally the visualization of the results heat map provided interpretability for diagnosis.

     

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