In medical fields it is very difficult and time-consuming to label samples. Only a small number of labeled samples or unlabeled samples are often encountered in medical fields. How to deal with this problem in medical diagnosis? Domain adaptation is an effective machine learning method to solve the scarce or no labeled samples problem. In this paper, an improved adversarial domain adaptation network is presented to solve this problem in tumor image diagnosis. We construct the tumor image diagnosis adversarial domain adaptation network framework. The framework consists of three parts: a feature extractor, a domain discriminator and a label predictor. The loss function of framework is designed in the paper. We design a feature extractor to acquire the domain invariant feature between the source domain and target domain. The feature extractor is an improved neural network consisting of several convolutional layers and sampling layers. The feature extractor pre-processes the input samples and gets the initial feature of the input space. The domain discriminator predicts the domain label and works adversarial with the feature extractor. The adversarial learning between the feature extractor and the domain discriminator acts as a regularization in the framework. The adversarial game can minimize the distance between the two domains and make sure the feature extractor learns the domain invariant feature. A label predictor is designed to classify the tumor image of the target domain based on the domain-invariant feature. The experiments on tumor image dataset are presented to validate the performance of proposed method. The experimental results show that the proposed method is superior to the other domain adaptation methods.

在医学领域，标记样品非常困难且耗时。在医学领域中，通常仅会遇到少量标记的样品或未标记的样品。在医学诊断中该如何处理？领域自适应是一种有效的机器学习方法，可以解决稀缺样本或没有标记样本的问题。本文提出了一种改进的对抗域自适应网络来解决肿瘤图像诊断中的这一问题。我们构建了肿瘤图像诊断对抗域适应网络框架。该框架由三部分组成：特征提取器，域识别器和标签预测器。本文设计了框架的损失函数。我们设计了一个特征提取器来获取源域和目标域之间的域不变特征。特征提取器是一种改进的神经网络，由几个卷积层和采样层组成。特征提取器对输入样本进行预处理，并获得输入空间的初始特征。域鉴别器预测域标签，并与特征提取器进行对抗。特征提取器和域鉴别器之间的对抗学习充当框架中的正则化。对抗博弈可以使两个域之间的距离最小化，并确保特征提取器学习域不变特征。标签预测子被设计为基于域不变特征对目标域的肿瘤图像进行分类。提出了在肿瘤图像数据集上的实验，以验证所提出方法的性能。