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Contemporary artificial neural networks (ANN) are trained end-to-end, jointlylearning both features and classifiers for the task of interest. Thoughenormously effective, this paradigm imposes significant costs in assemblingannotated task-specific datasets and training large-scale networks. We proposeto decouple feature learning from downstream lung ultrasound tasks byintroducing an auxiliary pre-task of visual biomarker classification. Wedemonstrate that one can learn an informative, concise, and interpretablefeature space from ultrasound videos by training models for predictingbiomarker labels. Notably, biomarker feature extractors can be trained fromdata annotated with weak video-scale supervision. These features can be used bya variety of downstream Expert models targeted for diverse clinical tasks(Diagnosis, lung severity, S/F ratio). Crucially, task-specific expert modelsare comparable in accuracy to end-to-end models directly trained for suchtarget tasks, while being significantly lower cost to train.

Learning Generic Lung Ultrasound Biomarkers for Decoupling Feature Extraction from Downstream Tasks