Dual Agreement Consistency Learning for Semi-Supervised Fetal Ultrasound Segmentation
Maternal-fetal US is the primary imaging modality for monitoring fetal development, yet accurate automated segmentation remains challenging due to the scarcity of pixel-level annotations. To address this issue, we propose DACL, a semi-supervised framework for robust fetal US image segmentation. DACL jointly trains a deployment-oriented lightweight convolutional network (1.47\thinsp\mathrm{M} parameters) and a Transformer-based network, leveraging labeled data for supervised learning and unlabeled data via CPS. To enhance prediction stability, we introduce a dual-agreement consistency loss that couples pixel-wise probabilistic divergence with entropy-guided confidence alignment. Unlike con...
| Authors | Fangyijie Wang, Guénolé Silvestre, Ziyang Wang et al. |
|---|---|
| Primary category | eess.IV |
| Published | 24 June 2026 |
| Last updated at source | 24 June 2026 |
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