论文标题
弱监督的对比度学习，以提高肺超声的严重程度评分
Weakly Supervised Contrastive Learning for Better Severity Scoring of Lung Ultrasound
论文作者
论文摘要
随着Covid-19的大流行的开始，超声已经成为患者床边监测的有效工具。因此，已经提供了大量的肺超声扫描，可用于基于AI的诊断和分析。已经提出了几种基于AI的患者严重性评分模型，这些模型依赖于对超声扫描的外观进行评分。 AI模型是使用超声表现严重性评分训练的，这些评分是根据标准化视觉特征手动标记的。我们解决了将视频剪辑中的每个超声框架标记的挑战。我们的对比学习方法将视频剪辑严重性标签视为单个帧的嘈杂的弱严重性标签，因此仅需要视频级标签。我们表明，它的性能要比常规的跨凝结损失训练更好。我们将框架的严重性预测与视频严重性预测相结合，并表明基于框架的模型在结合公共和私人资源的大型数据集上实现了与基于视频的TSM模型相当的性能。
With the onset of the COVID-19 pandemic, ultrasound has emerged as an effective tool for bedside monitoring of patients. Due to this, a large amount of lung ultrasound scans have been made available which can be used for AI based diagnosis and analysis. Several AI-based patient severity scoring models have been proposed that rely on scoring the appearance of the ultrasound scans. AI models are trained using ultrasound-appearance severity scores that are manually labeled based on standardized visual features. We address the challenge of labeling every ultrasound frame in the video clips. Our contrastive learning method treats the video clip severity labels as noisy weak severity labels for individual frames, thus requiring only video-level labels. We show that it performs better than the conventional cross-entropy loss based training. We combine frame severity predictions to come up with video severity predictions and show that the frame based model achieves comparable performance to a video based TSM model, on a large dataset combining public and private sources.
