Boundary-Aware Contrastive Learning for Single-Source Domain Generalization in Medical Image Segmentation
Chenbin Zhang, Zhiqiang Hu, Shuyu Dai, Qingyuan He, Defeng Liu, Kun Yan, Ping Wang · 2024
Domain shift is a prevalent issue for medical imaging in both the cross-modality and cross-site manner. As a result, the research of domain generalization meets a significant need that requires no access to any samples in novel target domains. In this paper, we propose BACON, a boundary-aware contrastive learning framework for single-source domain generalization, which enjoys the advantages of both the data-driven methods (e.g. augmentation) and inductive bias methods (e.g. normalization and whitening). BACON designs a novel data augmentation by integrating the bidirectional CutMix with the RandConv and introduces inductive bias through an innovative contrastive learning loss function. Experiments on both the cross-modality and cross-site scenario demonstrate the effectiveness, where BACON achieves a substantial improvement of 33.51% and 33.47% Dice scores over the baseline in the BraTS 2020 dataset and sets the new state of the art on the Prostate Cross-site dataset with an average Dice score of 72.51%.