  Self-supervised learning (SSL) has potential for effective representation
learning in medical imaging, but the choice of data augmentation is critical
and domain-specific. It remains uncertain if general augmentation policies suit
surgical applications. In this work, we automate the search for suitable
augmentation policies through a new method called Dimensionality Driven
Augmentation Search (DDA). DDA leverages the local dimensionality of deep
representations as a proxy target, and differentiably searches for suitable
data augmentation policies in contrastive learning. We demonstrate the
effectiveness and efficiency of DDA in navigating a large search space and
successfully identifying an appropriate data augmentation policy for
laparoscopic surgery. We systematically evaluate DDA across three laparoscopic
image classification and segmentation tasks, where it significantly improves
over existing baselines. Furthermore, DDA's optimised set of augmentations
provides insight into domain-specific dependencies when applying contrastive
learning in medical applications. For example, while hue is an effective
augmentation for natural images, it is not advantageous for laparoscopic
images.
