残差神经网络及其在医学图像处理中的应用研究

Research on Residual Neural Network and Its Application on Medical Image Processing

残差神经网络(ResNet)是近几年来深度学习研究中的热点,在计算机视觉领域取得较好成就.本文对残差神经网络从以下几个方面进行总结:第一,阐述残差神经网络的基本结构和工作原理;第二,在模型发展方面,以时间为顺序总结了残差神经网络的8种网络模型;第三,在结构优化方面,从残差神经网络的卷积层、池化层、残差单元、全连接层以及整个网络5个方面进行总结;最后,将ResNet应用到医学图像处理领域,主要从图像识别和图像分割2个方面探讨.本文对残差神经网络的原理、模型、结构进行了系统地总结,对残差神经网络的研究发展具有一定的积极意义.

Residual neural network(ResNet)has witnessed tremendous amount of attention in deep learning research over the last few years and has made great achievements in computer vision.In this paper,the ResNet is summarized in the following aspects:Firstly,the basic structure and working principle of the ResNet are expounded;Secondly,in model development,the eight network models of the ResNet are summarized in time sequence;Thirdly,in structural optimization,the research progress is described from five aspects of ResNet,including convolutional layer,pooling layer,residual unit,fully connected layer and the whole network;Finally,the application of ResNet in medical images processing is mainly discussed from two aspects of image recognition and image segmentation.In this paper,the principles,models,and structures of ResNet are systematically summarized,which has positive significance to the research and development of ResNet.