解剖锚定的自监督：蒸馏视觉基础模型用于不变超声表示
Anatomy-Anchored Self-Supervision: Distilling Vision Foundation Models for Invariant Ultrasound Representation
AI总结：
提出解剖锚定的超声自监督框架ANAUS，通过可学习潜在提示引擎和领域自适应实现无标注解剖分割，并设计双策略自监督学习（语义感知解剖分离对齐和上下文核心区域预测）来增强表示学习，在六个公开数据集上超越现有方法。
摘要：
自监督预训练范式在医学图像中学习可迁移表示方面日益重要，但现有超声图像方法在图像或帧级别操作，忽略了临床对齐表示学习的解剖上下文。在这项工作中，我们提出了一种解剖锚定的超声自监督框架ANAUS，将表示学习从通用视觉区域转移到临床有意义的解剖结构。利用可学习的潜在提示引擎以及对现有公开图像-掩码对的一次性领域自适应，我们使LP-SAM模块能够大规模实现无标注解剖描绘。基于此解剖基础，我们提出了一种双策略自监督学习范式，包括视图间语义感知的解剖分离对齐和上下文核心区域预测，以增强表示学习。具体而言，前者在相同解剖区域内强制特征不变性，同时促进不同结构间的可区分性；后者迫使模型重建被破坏的区域，从而捕获细粒度的结构细节。在六个公开数据集上的广泛评估表明，我们的方法持续优于当前最先进的方法，同时保持了临床部署所需的计算效率。代码可在https://github.com/zhcz328/ANAUS获取。
英文摘要：
Self-supervised pre-training paradigm has gained increasing prominence for learning transferable representations in medical imaging, yet existing methods for ultrasound (US) images operate at the image or frame level, overlooking the anatomical context for clinical-aligned representation learning. In this work, we propose an anatomy-anchored ultrasound self-supervision framework ANAUS that shifts representation learning from generic visual regions to clinically meaningful anatomical structures. Utilizing a learnable latent prompt engine alongside a one-time domain adaptation on existing public image-mask pairs, we empower the LP-SAM module to achieve annotation-free anatomy delineation at scale. Building upon this anatomical grounding, we propose a dual-policy self-supervised learning paradigm consisting of inter-view semantics-aware anatomy-separating alignment and contextual core-region prediction to enhance representation learning. Specifically, the former enforces feature invariance within identical anatomical regions while promoting discriminability across distinct structures; the latter compels the model to reconstruct corrupted regions, thereby capturing fine-grained structural details. Extensive evaluations on six public datasets demonstrate that ANAUS consistently outstrips current state-of-the-art methods while maintaining the computational efficiency essential for clinical deployment. Code is available at https://github.com/zhcz328/ANAUS.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
