Unsupervised domain adaptation with hyperbolic graph convolution network for segmentation of X-ray breast mass

Abstract

Deep learning has been widely used in medical image segmentation, such as breast tumor segmentation, prostate MR image segmentation, and so on. However, the labeling of the data set takes a lot of time. Although the emergence of unsupervised domain adaptation fills the technical gap, the existing domain adaptation methods for breast segmentation do not consider the alignment of the source domain and target domain breast mass structure. This paper proposes a hyperbolic graph convolutional network architecture. First, a hyperbolic graph convolutional network is used to make the source and target domains structurally aligned. Secondly, we adopt a hyperbolic space mapping model that has better expressive ability than Euclidean space in a graph structure. In particular, when constructing the graph structure, we added the completion adjacency matrix, so that the graph structure can be changed after each feature mapping, which can better improve the segmentation accuracy. Extensive comparative and ablation experiments were performed on two common breast datasets(CBIS-DDSM and INbreast). Experiments show that the method in this paper is better than the most advanced model. When CBIS-DDSM and INbreast are used as the source domain, the segmentation accuracy reaches 89.1% and 80.7%.