Exploring Image Augmentations for Siamese Representation Learning with Chest X-Rays

Understand

Image augmentations are quintessential for effective visual representation learning across self-supervised learning techniques.

• While augmentation strategies for natural imaging have been studied extensively, medical images are vastly different from their natural counterparts.
• Thus, it is unknown whether common augmentation strategies employed in Siamese representation learning generalize to medical images and to what extent.
• To address this challenge, in this study, we systematically assess the effect of various augmentations on the quality and robustness of the learned representations.