Dynamic adversarial domain adaptation based on multikernel maximum mean discrepancy for breast ultrasound image classification

Abstract

Due to the privacy of breast ultrasound images, it is difficult to obtain a dataset, which results in the lack of a large number of labeled datasets. Additionally, the difference in the feature distribution of datasets is also considered. In this paper, a dynamic adversarial domain adaptive network based on the multi kernel maximum mean discrepancy (MK_DAAN) is proposed. An adaptive layer is added to the model to further align the feature distribution of the datasets in the source and target domains, and the multi kernel maximum mean discrepancy is adopted in the adaptive distance measurement. The dual feature alignment of the adaptive layer and adversarial learning improves the classification performance of the model in breast ultrasound images. The experimental results show that the MK_DAAN model has better classification performance than typical unsupervised domain adaptation (DDC, DAN) and typical adjustment domain adaptation (DAAN, MADA) models.