  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.
