发表机构
Mohamed bin Zayed University of Artificial Intelligence; SEHA; Sheikh Khalifa Medical City; Khalifa University; Stanford University（穆罕默德·本·扎耶德人工智能大学; 赛哈; 谢赫·哈利法医疗城; 哈里发大学; 斯坦福大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对肺部超声视频分类的挑战，提出深度学习框架，采用层次感知训练与解剖引导学习，结合临床结构化目标与解剖监督，经实验验证该方法能提升病理分离、实现高宏F1，且具迁移适应性，是稳健可解释的视频分析实用方法。
AI 中文摘要
肺部超声（LUS）是评估因心力衰竭或肾功能受损而有风险患者肺水肿的床边工具。然而，由于斑点噪声、成像伪影和依赖操作员的采集变异性，自动LUS分析仍然具有挑战性。在这项工作中，我们提出了一个用于多类LUS视频分类的深度学习框架，该框架探索了两个组成部分：层次感知训练和解剖引导学习。从一个强大的基线开始，我们引入分层训练策略，然后引入胸膜线掩码监督，以引导模型关注与解剖相关的区域。我们使用来自219名患者的1886个视频的开放获取数据集研究四个临床相关类别——健康、B线、实变和伴有实变的混合B线，并通过患者级五折交叉验证进行评估。结果表明，层次感知训练相对于平面分类改善了病理分离，而掩码引导的注意力监督实现了65.7%的最高平均宏F1，并产生了更局部化的注意力模式。在外部COVID-BLUeS数据集上的迁移实验进一步显示了具有竞争力和参数效率的适应性，同时保留了以胸膜为重点的注意力行为。这些发现表明，将临床结构化目标与解剖引导监督相结合是一种用于稳健、可解释的LUS视频分析的实用方法。代码和模型实现可在该https URL上获得。
英文摘要
Lung ultrasound (LUS) is a bedside tool for assessing pulmonary edema in patients at risk due to heart failure or impaired kidney function. However, automated LUS analysis remains challenging because of speckle noise, imaging artifacts, and operator-dependent acquisition variability. In this work, we present a deep learning framework for multi-class LUS video classification that explores two components: hierarchy-aware training, and anatomy-guided learning. Starting from a strong baseline, we introduce hierarchical training strategies and then introduce pleural line mask supervision to guide model attention toward anatomically relevant regions. We study four clinically relevant classes--healthy, B-lines, consolidations, and mixed B-lines with consolidations--using an open-access dataset of 1,886 videos from 219 patients, evaluated with patient-level five-fold cross-validation. Results show that hierarchy-aware training improves pathological separation relative to flat classification, while mask-guided attention supervision achieves the highest mean macro-F1 of 65.7\% and produces more localized attention patterns. Transfer experiments on the external COVID-BLUeS dataset further show competitive and parameter-efficient adaptation while preserving pleural-focused attention behavior. These findings suggest that combining clinically structured objectives with anatomy-guided supervision is a practical approach to robust, interpretable LUS video analysis. Code and model implementations are available at https://github.com/Alya-Almsouti/LUS-video-classification.
