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

arXiv: https://arxiv.org/abs/2511.12559 · HTML: https://mlsanity.com/paper/2511.12559/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Medical and biomedical MoE, one of 12 directions in Mixture-of-experts.
- Builds on 8 of its 49 references that are in the map.
- First posted on arXiv 16 Nov 2025.

## Placed in

- Mixture-of-experts / [Medical and biomedical MoE](https://mlsanity.com/moe/medical-and-biomedical-moe/)

## Builds on (most cited first, 8 of 8)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://mlsanity.com/paper/2101.03961/) (2021)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [DiffMIC: Dual-Guidance Diffusion Network for Medical Image Classification](https://mlsanity.com/paper/2303.10610/) (2023)
- [Learning Hierarchical Image Segmentation For Recognition and By Recognition](https://mlsanity.com/paper/2210.00314/) (2022)
- [Exploring Cross-Domain Few-Shot Classification via Frequency-Aware Prompting](https://mlsanity.com/paper/2406.16422/) (2024)
- [A Tale of Two Classes: Adapting Supervised Contrastive Learning to Binary Imbalanced Datasets](https://mlsanity.com/paper/2503.17024/) (2025)

## Built on by (most cited first, 3 of 3)

- [Rethinking Air-Ground Collaboration: A Progressive Cross-Task Benchmark and Socialized Learning Framework](https://mlsanity.com/paper/2606.18841/) (2026)
- [ForceU-VLA: A Force-Aware Vision-Language-Action Model for Embodied Ultrasound Scanning](https://mlsanity.com/paper/2608.15009/) (2026)
- [US-VLA: An Ultrasound Vision-Language-Action Model for Embodied Abdomina](https://mlsanity.com/paper/2608.16074/) (2026)

## Abstract (arXiv)

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.