Multi-center breast ultrasound images eg mentation aims to leverage limited labeled data from a single center to enhance model discriminability across unlabeled data from other centers. However, differences in equipment parameters, disease severity, and imaging conditions collectively contribute to significant cross domain shifts in multi-center data. In a spirit of the golden mean, we argue that constructing an intermediate domain between the source and target domains can effectively improve model generalization. Therefore, we propose a Cross-domain Few-label Generalization (CFG) framework for multi-center breast ultrasound image segmentation. Specifically, we design the Intermediate Domain Generator (IDG) to generate intermediate domain samplesthatcontain features from both the source and target domains bidirectionally, enabling the model to explicitly learn univer sal semantic representations. Additionally, we apply Swin Masked Autoencoder (MAE) to mask and reconstruct ul trasound images, simulating speckle noise encountered during clinical ultrasound acquisition, thereby increasing the diversity of intermediate domain samples. Further more, we integrate the Kolmogorov-Arnold Network (KAN) with UNet to construct KAN-UNet, integrating learnable spline functions directly onto the edges, enabling effective multi-scale perception of breast cancer lesion features. Experimental results show that even with limited labeled data from the source domain (BUSI-WHU), the CFG frame work achieves a Kappa value of 77.17%, surpassing ten state-of-the-art methods and outperforming the second best method by 0.78% across four multi-center ultrasound datasets (BUSI-WHU, BUSI, Dataset-B, and Dataset-C) collected from different medical centers. The code is available at https://github.com/yzygit1230/CFG.
Mei et al. (Thu,) studied this question.