Adversarial domain adaptation network for tumor image diagnosis

Abstract

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.