ID 原文 译文
52627 由于无法获得原神经网络模型结构，本文根据原模型的输入输出数据以及经典的神经网络模型结构，构建了原模型的预测模型。 Since the structure of original neural network model cannot be obtained, this paper constructs a prediction model of the original model based on the input and output data of the original model and the classic structure of neural network model.
52628 该方法主要通过对预测模型的结构参数进行编码，并利用遗传算法(GA)进行选择、交叉、变异操作，从而构建出原模型的等价模型。 This method mainly encodes the structural parameters of the prediction model, and uses genetic algorithm(GA) to perform selection, crossover, and mutation operations to construct an equivalent model of the original model.
52629 对于同一输入数据，等价模型和原模型的输出基本保持一致。 For the same input data, the output of the equivalent model and the original model are basically the same.
52630 本文提出的构建方法在图像分类、信号调制类型分类和网络链路预测领域均取得了较好的效果。 The construction method proposed in this paper has achieved good results in the fields of image classification, signal modulation type classification and network link prediction.
52631 目前新冠肺炎(COVID-19)在全球蔓延，为了对新冠肺炎进行早期诊断，同时减轻医护人员的工作压力，使用深度学习对患者胸部电子计算机断层扫描(CT)图像进行分析变得越来越重要。 The coronavirus disease 2019(COVID-19) has spread worldwide. To early diagnose COVID-19 and reduce the pressure of medical staff, using deep learning methods to analyze chest computed tomography(CT) images of patients becomes more and more important.
52632 针对肺炎图像中纹理细节较为丰富、边缘结构模糊、极易干扰机器及医生诊断的问题，本文提出一种基于多尺度并行深度可拆分卷积神经网络(MSP-ReCNN)，对新冠肺炎CT图像进行去噪处理，提升肺炎图像质量。 The images of pneumonia have rich texture details and fuzzy edge structure, which are easy to interfere with the diagnosis of machine and doctor. COVID-19 CT images denoising method based on multi-scale parallel deep split convolution neural network(MSP-ReCNN) is proposed in this paper to enhance the quality of pneumonia images.
52633 多尺度特征提取模块从不同尺度提取肺炎图像中的纹理特征细节，采用深浅通道并行方式，分别提取肺炎图像中的高维度以及低维度的特征。 Multi scale feature extraction module can extract the details of texture features in pneumonia images from different scales. The parallel method of deep and shallow channels are utilized to extract the high-dimensional and low-dimensional features of pneumonia images.
52634 为进一步优化网络模型，提出一种拆分卷积方式，可将特征图拆分为两类，一类为主要关注特征，另一类为次要关注特征。 To further optimize the network model, the split convolution method is proposed. The feature graph can be divided into two categories, one is the primary concern feature, the other is the secondary concern feature.
52635 使用复杂度高的计算方式从主要关注特征中提取关键信息，对于次要关注特征，则采取复杂度低的计算方式提取补偿信息。 High complexity computing method is used to extract the core information from the primary concern features, and the low complexity calculation method is used to extract the compensation information for others.
52636 通过与非局部均值(NLM)去噪算法、收缩卷积神经网络(SCNN)深度模型、去噪卷积神经网络(Dn CNN)深度模型对比，以及网络消融实验，可以看出本文提出的模型能有效去除肺炎图像中的噪声，并且可以更好地保留原始图像中的纹理结构细节，为机器以及医生提供更可靠的辅助诊断。 Compared with non-local mean( NLM) denoising algorithm, shrinkage convdutional neural network(SCNN) model, denosing convolutional neural network(DnCNN) model, and through network ablation experiments, it can be drawn that the proposed model can effectively remove the noise in COVID-19 CT images, and can retain the texture structure details of the original image, as well as provide more reliable auxiliary diagnosis for machines and doctors.