  Self-supervised learning methods based on data augmentations, such as SimCLR,
BYOL, or DINO, allow obtaining semantically meaningful representations of image
datasets and are widely used prior to supervised fine-tuning. A recent
self-supervised learning method, $t$-SimCNE, uses contrastive learning to
directly train a 2D representation suitable for visualisation. When applied to
natural image datasets, $t$-SimCNE yields 2D visualisations with semantically
meaningful clusters. In this work, we used $t$-SimCNE to visualise medical
image datasets, including examples from dermatology, histology, and blood
microscopy. We found that increasing the set of data augmentations to include
arbitrary rotations improved the results in terms of class separability,
compared to data augmentations used for natural images. Our 2D representations
show medically relevant structures and can be used to aid data exploration and
annotation, improving on common approaches for data visualisation.
