ResNet及其在医学图像处理领域的应用:研究进展与挑战

残差神经网络(ResNet)是深度学习领域的研究热点，广泛应用于医学图像处理领域。该文对残差神经网络从以下几个方面进行综述:首先，阐述残差神经网络的基本原理和模型结构；然后，从残差单元、残差连接和网络整体结构3方面总结了残差神经网络的改进机制；其次，从与DenseNet, U-Net, Inception结构和注意力机制结合4方面探讨残差神经网络在医学图像处理领域中的广泛应用；最后，讨论ResNet在医学图像处理领域中面临的主要挑战，并对未来的发展方向进行展望。该文系统梳理了残差神经网络的最新研究进展，以及在医学图像处理中的应用，对残差神经网络的研究具有重要的参考价值。

ResNet and Its Application to Medical Image Processing: Research Progress and Challenges

Residual neural Network (ResNet) is a hot topic in deep learning research, which is widely used in medical image processing. The residual neural network is reviewed in this paper from the following aspects: Firstly, the basic principles and model structure of residual neural network are explained; Secondly, the improvement mechanisms of residual neural network are summarized from three aspects of residual unit, residual connection and the entire network structure; Thirdly, the wide applications of residual neural network to medical image processing are discussed from four aspects combining DenseNet, U-Net, Inception structure and attention mechanism; Finally, the main challenges that ResNet faces in medical image processing are discussed, and the future development direction is prospected. In this paper, the latest research progress of residual neural network and its application to medical image processing are systematically sorted out, which has important reference value for the research of residual neural network.