SEMC: Structure-Enhanced Mixture-of-Experts Contrastive Learning for Ultrasound Standard Plane Recognition

TL;DR

SEMC introduces a structure-aware contrastive learning framework with a novel fusion module and mixture-of-experts mechanism, significantly improving ultrasound standard plane recognition by leveraging multi-scale structural details and hierarchical feature discrimination.

Contribution

The paper proposes SEMC, a novel contrastive learning framework that enhances ultrasound plane recognition by integrating structure-aware feature fusion and a mixture-of-experts approach for hierarchical contrastive learning.

Findings

SEMC outperforms state-of-the-art methods on multiple datasets.

The Semantic-Structure Fusion Module effectively captures fine-grained structural details.

The Mixture-of-Experts Contrastive Recognition Module improves class separability.

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, ultimately resulting in suboptimal recognition of both structural and discriminative details in ultrasound standard planes. To address these issues, we propose SEMC, a novel Structure-Enhanced Mixture-of-Experts Contrastive learning framework that combines structure-aware feature fusion with expert-guided contrastive learning. Specifically, we first introduce a novel Semantic-Structure Fusion Module (SSFM) to exploit multi-scale structural information and enhance the model's ability to perceive fine-grained structural details by effectively aligning shallow and deep features. Then, a novel Mixture-of-Experts Contrastive Recognition Module (MCRM) is designed to perform hierarchical contrastive learning and classification across multi-level features using a mixture-of-experts (MoE) mechanism, further improving class separability and recognition performance. More importantly, we also curate a large-scale and meticulously annotated liver ultrasound dataset containing six standard planes. Extensive experimental results on our in-house dataset and two public datasets demonstrate that SEMC outperforms recent state-of-the-art methods across various metrics.