<ul><li>A study investigated the impact of data augmentation and preprocessing strategies in self-supervised learning for lung ultrasound.</li><li>Semantics-preserving data augmentation resulted in the greatest performance for COVID-19 classification, while cropping-based methods yielded the greatest performance on B-line and pleural effusion object classification tasks.</li><li>Increased downstream performance for multiple tasks was observed with semantics-preserving ultrasound image preprocessing.</li><li>Guidance regarding data augmentation and preprocessing strategies in self-supervised learning for ultrasound was provided.</li></ul>

The Efficacy of Semantics-Preserving Transformations in Self-Supervised Learning for Medical Ultrasound

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