DDA: Dimensionality Driven Augmentation Search for Contrastive Learning in Laparoscopic Surgery
This work addresses the challenge of domain-specific data augmentation for self-supervised learning in medical imaging, particularly laparoscopic surgery, offering an incremental improvement over existing methods.
The authors tackled the problem of automating data augmentation search for self-supervised contrastive learning in laparoscopic surgery, proposing DDA, which uses local dimensionality as a proxy to efficiently find domain-specific policies and significantly improves performance over baselines in classification and segmentation tasks.
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.