卷积神经网络的原理及其在医学影像诊断中的应用

利用卷积神经网络快速高效地对医学影像数据进行分析和处理可以实现医学影像数据快速分类、定位等操作，提高医学诊疗的效率。本研究从卷积神经网络的背景和原理入手，介绍各种类型的卷积神经网络的应用场景和一些常用的卷积神经网络模型，包括残差卷积神经网络、U-net、循环卷积神经网络等及其在医学影像诊断中的应用，最后针对卷积神经网络和人工智能技术讨论了其未来的展望和挑战。

Principle of convolutional neural network and its applications in medical imaging diagnosis

Abstract: Convolutional neural network can realize the localization, classification and other tasks for the large amounts of medical imaging data by analyzing and processing the medical imaging data swiftly and efficiently, thereby improving the efficiency of medical diagnosis and treatment. Starting from the introduction on the background and principle of convolutional neural network, the application scenarios of various types of convolutional neural networks are presented, and the applications of some commonly-used neural network models, including residual convolutional neural network, U-net, recurrent convolutional neural network in medical imaging diagnosis are summarized. Finally, the future prospects and challenges of convolutional neural network and artificial intelligence are discussed.