医学图像分析深度学习方法研究与挑战

Deep Learning in Medical Image Analysis and Its Challenges

深度学习(Deep learning,DL),特别是深度卷积神经网络(Convolutional neural networks,CNNs),能够从医学图像大数据中自动学习提取隐含的疾病诊断特征,近几年已迅速成为医学图像分析研究热点.本文首先简述医学图像分析特点;其次,论述深度学习基本原理,总结深度CNNs在医学图像分析中的分类、分割框架;然后,分别论述深度学习在医学图像分类、检测、分割等各应用领域的国内外研究现状;最后,探讨归纳医学图像分析深度学习方法挑战及其主要应对策略和开放的研究方向.

Deep learning （DL） algorithms, such as convolutional neural networks （CNNs）, can automatically extract hidden disease diagnosis features from medical image data, and are being used to analyze medical images now. We review most of the deep learning methods for medical image analysis. Firstly, we introduce the characteristics of medical image analysis briefly. Then, we analyze the principles of deep learning, highlight the popular CNNs and summarize the frameworks of image classification and segmentation. Thirdly, we describe the state-of-the-art of the medical image analysis methods based on deep learning. Finally, we discuss the challenges and practicable strategies in deep learning for medical image analysis, as well as open research.