Synthetic Privileged Information Enhances Medical Image Representation
  Learning
Lucas Farndale Chris Walsh Robert Insall Ke Yuan
Multimodal self-supervised representation learning has consistently proven to
be a highly effective method in medical image analysis, offering strong task
performance and producing biologically informed insights. However, these
methods heavily rely on large, paired datasets, which is prohibitive for their
use in scenarios where paired data does not exist, or there is only a small
amount available. In contrast, image generation methods can work well on very
small datasets, and can find mappings between unpaired datasets, meaning an
effectively unlimited amount of paired synthetic data can be generated. In this
work, we demonstrate that representation learning can be significantly improved
by synthetically generating paired information, both compared to training on
either single-modality (up to 4.4x error reduction) or authentic multi-modal
paired datasets (up to 5.6x error reduction).