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arXiv AI By Alisher Myrgyyassov, Zhen Song, Bruce Xiao Wang, Yu Sun, Min Ney Wong, Yihao Zhou, Yongping Zheng

Dual Co-Train: Cross-Dataset Ultrasound Tongue Segmentation Under Extreme Data Scarcity

Read the original on arXiv AI →

The paper introduces Dual Co-Train, a source‑free domain adaptation framework for ultrasound tongue segmentation that operates under extreme data scarcity. Starting from a lightweight UltraUNet pretrained on only five labeled images, the method iteratively refines pseudo‑labels, filters unreliable masks with a contour‑based quality‑control module, and generates target‑style synthetic image‑mask pairs via a segmentation‑guided conditional GAN. The student model is trained on a mix of clean pseudo‑labeled target images, noisy pseudo‑labels with consistency regularization, and synthetic samples, enabling closed‑loop adaptation without access to source data. Experiments on 12 source‑target transfer pairs across eight datasets show that this approach improves segmentation overlap and contour accuracy over both unsupervised baselines and supervised models.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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