研究人员提出Dual Co-Train框架，只需5张标记图像就能实现跨数据集超声波舌部分割，效果优于监督方法。
研究人员提出Dual Co-Train框架，仅需5个标记源图像即可实现超声波舌部分割。该方法通过迭代优化伪标签、使用基于轮廓的质量控制模块过滤不可靠掩码，以及通过分割引导的条件GAN生成目标风格合成图像-掩码对。在8个超声波舌成像数据集的12个源-目标转移对上评估，该方法在分割重叠率和轮廓准确性上优于包括监督方法在内的基线。
Dual Co-Train: Cross-Dataset Ultrasound Tongue Segmentation Under Extreme Data Scarcity
Ultrasound tongue contour segmentation remains challenging under cross-dataset domain shift, where limited annotations, probe variability, and acquisition noise often degrade model generalization. We present a source-free domain adaptation framework for robust ultrasound tongue segmentation built on a lightweight UltraUNet backbone. Starting from a checkpoint pretrained on only five labeled source images, simulating an underfitted constrained source model, the proposed method adapts to a fully-unlabeled target domain by iteratively refining pseudo-labels, filtering unreliable masks with a contour-based quality-control module, and generating target-style synthetic image-mask pairs through a segmentation-guided conditional GAN. The student model is then trained on a mixture of clean pseudo-labeled target images, noisy pseudo-labels with consistency regularization, and synthetic samples, enabling closed-loop adaptation without access to source data. We evaluate the method on 12 source-target transfer pairs across eight ultrasound tongue imaging datasets, and conduct source-size scaling experiments and ablation studies. Across all comparisons, the proposed framework improves segmentation overlap and contour accuracy over the baselines, including supervised ones. These results suggest that task-specific pseudo-label refinement and synthetic target-style augmentation can substantially improve source-free adaptation for ultrasound tongue imaging.