  Self-supervised pretraining has been observed to improve performance in
supervised learning tasks in medical imaging. This study investigates the
utility of self-supervised pretraining prior to conducting supervised
fine-tuning for the downstream task of lung sliding classification in M-mode
lung ultrasound images. We propose a novel pairwise relationship that couples
M-mode images constructed from the same B-mode image and investigate the
utility of data augmentation procedure specific to M-mode lung ultrasound. The
results indicate that self-supervised pretraining yields better performance
than full supervision, most notably for feature extractors not initialized with
ImageNet-pretrained weights. Moreover, we observe that including a vast volume
of unlabelled data results in improved performance on external validation
datasets, underscoring the value of self-supervision for improving
generalizability in automatic ultrasound interpretation. To the authors' best
knowledge, this study is the first to characterize the influence of
self-supervised pretraining for M-mode ultrasound.
