  Medical imaging technologies are generating increasingly large amounts of
high-quality, information-dense data. Despite the progress, practical use of
advanced imaging technologies for research and diagnosis remains limited by
cost and availability, so information-sparse data such as H&E stains are relied
on in practice. The study of diseased tissue requires methods which can
leverage these information-dense data to extract more value from routine,
information-sparse data. Using self-supervised deep learning, we demonstrate
that it is possible to distil knowledge during training from information-dense
data into models which only require information-sparse data for inference. This
improves downstream classification accuracy on information-sparse data, making
it comparable with the fully-supervised baseline. We find substantial effects
on the learned representations, and this training process identifies subtle
features which otherwise go undetected. This approach enables the design of
models which require only routine images, but contain insights from
state-of-the-art data, allowing better use of the available resources.
