  Often, applications of self-supervised learning to 3D medical data opt to use
3D variants of successful 2D network architectures. Although promising
approaches, they are significantly more computationally demanding to train, and
thus reduce the widespread applicability of these methods away from those with
modest computational resources. Thus, in this paper, we aim to improve standard
2D SSL algorithms by modelling the inherent 3D nature of these datasets
implicitly. We propose two variants that build upon a strong baseline model and
show that both of these variants often outperform the baseline in a variety of
downstream tasks. Importantly, in contrast to previous works in both 2D and 3D
approaches for 3D medical data, both of our proposals introduce negligible
additional overhead over the baseline, improving the democratisation of these
approaches for medical applications.
