针对超声图像特点设计高效预训练方法，提升胎儿超声诊断精度
PolarMAE: Efficient Fetal Ultrasound Pre-training via Semantic Screening and Polar-Guided Masking
- 通过语义筛选动态提取关键图像，减少冗余扫描数据影响
- 引入声学区域约束，强制模型聚焦有效成像区域而非背景
- 基于极坐标纹理协同掩码，捕捉超声特有的径向成像规律
智能胎儿超声解读对产前诊断至关重要，但高标注成本与操作者差异导致无监督预训练成为重要方向。现有方法普遍忽略超声特有属性——严重数据冗余、扇形局部性及极坐标波束成形，限制了下游任务表现。为此，我们提出PolarMAE，一种专为超声图像设计的高效预训练框架。为缓解连续扫描冗余，引入渐进式视觉-语义筛选（PVSS），自适应提取高价值样本，显著提升预训练效率。进一步设计声学限定区域约束（ABRC），强制模型聚焦有效声学区域而非无效暗背景。最后，结合波束成形先验与局部细节，提出极坐标-纹理协同掩码（PTCM），使模型能捕捉深层径向成像模式与关键组织结构。跨多数据集与下游解读任务的实验表明，该方法在性能与可扩展性上均达到当前最优。
原文摘要 · Abstract (English)
Intelligent fetal ultrasound (US) interpretation is crucial for prenatal diagnosis, but high annotation costs and operator-induced variance make unsupervised pre-training a highly promising paradigm. However, existing pre-training methods largely ignore US-specific characteristics -- severe data redundancy, fan-shaped locality, and polar coordinate beamforming -- limiting their effectiveness in downstream tasks. To address this, we propose PolarMAE, a novel and efficient pre-training framework tailored for US images. Specifically, to mitigate continuous scanning redundancy, we introduce a Progressive Visual-Semantic Screening (PVSS) that adaptively extracts high-value samples, significantly boosting pre-training efficiency. Furthermore, we design an Acoustic-Bounded Region Constraint (ABRC) to accommodate US locality, forcing the model to focus strictly on valid acoustic regions rather than invalid dark backgrounds. Finally, leveraging the beamforming prior and local details, we propose a Polar-Texture Collaborative Masking (PTCM), enabling the model to capture underlying radial imaging patterns and critical tissue structures. Extensive experiments across diverse datasets and downstream interpretation tasks demonstrate that our method achieves state-of-the-art performance with strong pre-training scalability and efficiency.
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