  Masked Image Modelling (MIM) has been shown to be an efficient
self-supervised learning (SSL) pre-training paradigm when paired with
transformer architectures and in the presence of a large amount of unlabelled
natural images. The combination of the difficulties in accessing and obtaining
large amounts of labeled data and the availability of unlabelled data in the
medical imaging domain makes MIM an interesting approach to advance deep
learning (DL) applications based on 3D medical imaging data. Nevertheless, SSL
and, in particular, MIM applications with medical imaging data are rather
scarce and there is still uncertainty. around the potential of such a learning
paradigm in the medical domain. We study MIM in the context of Prostate Cancer
(PCa) lesion classification with T2 weighted (T2w) axial magnetic resonance
imaging (MRI) data. In particular, we explore the effect of using MIM when
coupled with convolutional neural networks (CNNs) under different conditions
such as different masking strategies, obtaining better results in terms of AUC
than other pre-training strategies like ImageNet weight initialization.
