A confidence-guided Unsupervised domain adaptation network with pseudo-labeling and deformable CNN-transformer for medical image segmentation.

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Abstract

Unsupervised domain adaptation (UDA) methods have achieved significant progress in medical image segmentation. Nevertheless, the significant differences between the source and target domains remain a daunting barrier, creating an urgent need for more robust cross-domain solutions. Current UDA techniques generally employ a fixed, unvarying feature alignment procedure to reduce inter-domain differences throughout the training process. This rigidity disregards the shifting nature of feature distributions throughout the training process, leading to suboptimal performance in boundary delineation and detail retention on the target domain. A novel confidence-guided unsupervised domain adaptation network (CUDA-Net) is introduced to overcome persistent domain gaps, adapt to shifting feature distributions during training, and enhance boundary delineation in the target domain. This proposed network adaptively aligns features by tracking cross-domain distribution shifts throughout training, starting with adversarial alignment at early stages (coarse) and transitioning to pseudo-label-driven alignment at later stages (fine-grained), thereby leading to more accurate segmentation in the target domain. A confidence-weighted mechanism then refines these pseudo labels by prioritizing high-confidence regions while allowing low-confidence areas to be gradually explored, thereby enhancing both label reliability and overall model stability. Experiments on three representative medical image datasets, namely MMWHS17, BraTS2021, and VS-Seg, confirm the superiority of CUDA-Net. Notably, CUDA-Net outperforms eight leading methods in terms of overall segmentation accuracy (Dice) and boundary extraction precision (ASD), highlighting that it offers an efficient and reliable solution for cross-domain medical image segmentation.

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