Contemporary artificial neural networks (ANN) are trained end-to-end, jointly learning both features and classifiers for the task of interest. Though enormously effective, this paradigm imposes significant costs in assembling annotated task-specific datasets and training large-scale networks. We propose to decouple feature learning from downstream lung ultrasound tasks by introducing an auxiliary pre-task of visual biomarker classification. We demonstrate that one can learn an informative, concise, and interpretable feature space from ultrasound videos by training models for predicting biomarker labels. Notably, biomarker feature extractors can be trained from data annotated with weak video-scale supervision. These features can be used by a variety of downstream Expert models targeted for diverse clinical tasks (Diagnosis, lung severity, S/F ratio). Crucially, task-specific expert models are comparable in accuracy to end-to-end models directly trained for such target tasks, while being significantly lower cost to train.

该研究提出了一种通过引入辅助的预任务(视觉生物标志分类)来将特征学习与下游肺部超声任务分离的方法；通过为预测生物标志标签训练模型，可以学习到一种信息丰富、简洁和可解释的超声视频特征空间。这些特征可以被用于各种下游临床任务的专家模型中，这些任务包括诊断、肺部严重程度以及S/F比；与直接针对这些任务进行训练的端到端模型相比，这些任务特定的专家模型具有可比的准确度，同时成本显著降低。

学习通用肺部超声生物标志物以解耦特征提取与下游任务