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.

本文旨在改进标准的 2D 自监督学习算法，通过对数据集的内在 3D 特性进行建模来解决在 3D 医疗数据中应用的计算资源问题，并且提出了两个改进版本，证明在各种下游任务中，这两个版本往往优于基准模型，同时也几乎不带来额外的开销，提高了医疗应用中这些方法的普及性。