SHE-Net: Semantic-Guided Hierarchical Experts for Ultrasound Standard Plane Recognition

Ultrasound standard plane recognition is an essential yet challenging task in clinical screening and diagnosis, owing to substantial intra-class variations and inter-class similarities caused by imaging noise, operator dependency, and anatomical variability. Existing methods mainly rely on visual appearance modeling, which may struggle to capture subtle structural differences and often overlook explicit anatomical semantics relevant to standard plane recognition. To address these challenges, we propose SHE-Net, a Semantic-guided Hierarchical Experts Network for ultrasound standard plane recognition. We first construct multi-granularity textual descriptions for existing datasets, providing explicit anatomical semantics from coarse plane categories to fine-grained structural characteristics. Based on these descriptions, we develop a Hierarchical Vision-Language Alignment (HVLA) strategy to align visual representations at different hierarchical levels with corresponding textual semantics, introducing structured semantic supervision into visual representation learning. Furthermore, we propose a Semantic-Routed Anatomical Experts (SRAE) module, which uses semantic-aware routing to dynamically assign visual representations to specialized experts, enabling the learning of complementary anatomical patterns from local structures to global plane configurations. By jointly integrating hierarchical semantic alignment and adaptive expert specialization, SHE-Net enhances the discriminability and structural consistency of ultrasound standard plane representations. In addition, we construct and annotate a new kidney ultrasound standard plane dataset to evaluate the generalizability of the proposed framework. Extensive experiments on three ultrasound standard plane datasets demonstrate that SHE-Net consistently outperforms existing methods across multiple evaluation metrics, highlighting the effectiveness of hierarchical anatomical semantics and semantic-guided expert specialization for robust ultrasound standard plane recognition.