这项研究解决了胎儿心脏超声分析中标注数据稀缺的痛点，做医学影像AI的团队可以直接参考其半监督框架和开源代码，值得关注。
该研究提出了一种半监督框架，用于胎儿心脏超声图像的联合分割与分类。方法基于EchoCare多任务骨干网络，集成SAM-Med2D进行边界细化，并利用DINOv3提升伪标签质量。引入视图特定硬掩码和两阶段优化策略：EMA阶段巩固分割能力，分类微调阶段冻结分割参数并重置分类头，以恢复分类性能而不损害分割效果。在FETUS 2026排行榜上，该方法达到79.99%的Dice系数、61.62%的归一化表面距离和41.20%的F1分数，验证了其在产前先天性心脏病筛查中的有效性。代码已开源。
Synergistic Foundation Models for Semi-Supervised Fetal Cardiac Ultrasound Analysis: SAM-Med2D Boundary Refinement and DINOv3 Semantic Enhancement
We present a semi-supervised framework for joint segmentation and classification of fetal cardiac ultrasound images. Built upon the EchoCare multi-task backbone, our method integrates SAM-Med2D for boundary refinement and leverages DINOv3 to enhance pseudo-label quality. We introduce view-specific hard masking along with a two-stage optimization strategy: an EMA phase to consolidate segmentation capabilities, followed by a Classification Fine-Tuning phase that freezes segmentation parameters and resets the classification head to recover classification performance without compromising segmentation gains. Evaluated on the FETUS 2026 leaderboard, our method achieves a Dice Similarity Coefficient at 79.99%, Normalized Surface Distance at 61.62%, and F1-score at 41.20%, validating the effectiveness of our approach for prenatal congenital heart disease screening. Source code is publicly available at: https://github.com/2826056177/zcst_fetus2026.