  In this study, we investigated whether self-supervised pretraining could
produce a neural network feature extractor applicable to multiple
classification tasks in B-mode lung ultrasound analysis. When fine-tuning on
three lung ultrasound tasks, pretrained models resulted in an improvement of
the average across-task area under the receiver operating curve (AUC) by 0.032
and 0.061 on local and external test sets respectively. Compact nonlinear
classifiers trained on features outputted by a single pretrained model did not
improve performance across all tasks; however, they did reduce inference time
by 49% compared to serial execution of separate fine-tuned models. When
training using 1% of the available labels, pretrained models consistently
outperformed fully supervised models, with a maximum observed test AUC increase
of 0.396 for the task of view classification. Overall, the results indicate
that self-supervised pretraining is useful for producing initial weights for
lung ultrasound classifiers.
