Classify epithelium‐stroma in histopathological images based on deep transferable network
Classify epithelium‐stroma in histopathological images based on deep transferable network
复制标题
DOI：
10.1111/jmi.12705
复制
发表时间：
2018-08
影响因子：
2
通讯作者：
Xian Yu;Huiru Zheng;Chi Liu;Yizhong Huang;Xinghao Ding
中科院分区：
文献类型：
--
作者：
Xian Yu;Huiru Zheng;Chi Liu;Yizhong Huang;Xinghao Ding
近年来，深度学习方法在组织病理学图像分析中受到了越来越多的关注。然而，传统的深度学习方法假设训练数据和测试数据具有相同的分布，这在真实的世界组织病理学应用中造成了一定的限制。然而，即使对于类似的任务，为每个指定的图像采集过程选择大量标记的组织学数据来训练新的神经网络也是昂贵的。本文将无监督域自适应引入到典型的深度卷积神经网络（CNN）模型中，以减轻标签的重复。无监督域自适应是通过将两个正则化项（即基于特征的自适应和熵最小化）添加到广泛使用的CNN模型（称为AlexNet）的目标函数来实现的。使用三个独立的公共上皮-基质数据集来验证所提出的方法。实验结果表明，在上皮-基质分类中，所提出的方法可以实现比常用的深度学习方法和一些现有的深度域自适应方法更好的性能。因此，所提出的方法可以被认为是组织病理学图像分析的真实的世界应用的更好选择，因为不需要为每个指定的域重新收集大规模标记数据。
Recently, the deep learning methods have received more attention in histopathological image analysis. However, the traditional deep learning methods assume that training data and test data have the same distributions, which causes certain limitations in real‐world histopathological applications. However, it is costly to recollect a large amount of labeled histology data to train a new neural network for each specified image acquisition procedure even for similar tasks. In this paper, an unsupervised domain adaptation is introduced into a typical deep convolutional neural network (CNN) model to mitigate the repeating of the labels. The unsupervised domain adaptation is implemented by adding two regularisation terms, namely the feature‐based adaptation and entropy minimisation, to the object function of a widely used CNN model called the AlexNet. Three independent public epithelium‐stroma datasets were used to verify the proposed method. The experimental results have demonstrated that in the epithelium‐stroma classification, the proposed method can achieve better performance than the commonly used deep learning methods and some existing deep domain adaptation methods. Therefore, the proposed method can be considered as a better option for the real‐world applications of histopathological image analysis because there is no requirement for recollection of large‐scale labeled data for every specified domain.