  Self-supervised learning (SSL) methods have shown promise for medical imaging
applications by learning meaningful visual representations, even when the
amount of labeled data is limited. Here, we extend state-of-the-art contrastive
learning SSL methods to 2D+time medical ultrasound video data by introducing a
modified encoder and augmentation method capable of learning meaningful
spatio-temporal representations, without requiring constraints on the input
data. We evaluate our method on the challenging clinical task of identifying
lung consolidations (an important pathological feature) in ultrasound videos.
Using a multi-center dataset of over 27k lung ultrasound videos acquired from
over 500 patients, we show that our method can significantly improve
performance on downstream localization and classification of lung
consolidation. Comparisons against baseline models trained without SSL show
that the proposed methods are particularly advantageous when the size of
labeled training data is limited (e.g., as little as 5% of the training set).
