  Computational pathology models rarely utilise data that will not be available
for inference. This means most models cannot learn from highly informative data
such as additional immunohistochemical (IHC) stains and spatial
transcriptomics. We present TriDeNT, a novel self-supervised method for
utilising privileged data that is not available during inference to improve
performance. We demonstrate the efficacy of this method for a range of
different paired data including immunohistochemistry, spatial transcriptomics
and expert nuclei annotations. In all settings, TriDeNT outperforms other
state-of-the-art methods in downstream tasks, with observed improvements of up
to 101%. Furthermore, we provide qualitative and quantitative measurements of
the features learned by these models and how they differ from baselines.
TriDeNT offers a novel method to distil knowledge from scarce or costly data
during training, to create significantly better models for routine inputs.
