SEMC: Structure-Enhanced Mixture-of-Experts Contrastive Learning for Ultrasound Standard Plane Recognition
Qing Cai, GuihaoYan, Fan Zhang, Cheng Zhang, Zhi Liu
Abstract
Ultrasound standard plane recognition is essential for clinical tasks such as disease screening, organ evaluation, and biometric measurement. However, existing methods fail to effectively exploit shallow structural information and struggle to capture fine-grained semantic differences through contrastive samples generated by image augmentations, leading to poor recognition of structural and discriminative details in ultrasound standard planes. To address these issues, we propose Structure-Enhanced Mixture-of-Experts Contrastive Learning (SEMC), a novel framework that combines structure-aware feature fusion with expert-guided contrastive learning. Specifically, we propose a Semantic-Structure Fusion Module (SSFM) to exploit multi-scale structural information and enhance the model
BibTeX
@inproceedings{aaai2026_semcstructureenh,
title = {SEMC: Structure-Enhanced Mixture-of-Experts Contrastive Learning for Ultrasound Standard Plane Recognition},
author = {Qing Cai and GuihaoYan and Fan Zhang and Cheng Zhang and Zhi Liu},
booktitle = {AAAI 2026},
year = {2026}
}