  In medical image analysis, the cost of acquiring high-quality data and their
annotation by experts is a barrier in many medical applications. Most of the
techniques used are based on supervised learning framework and need a large
amount of annotated data to achieve satisfactory performance. As an
alternative, in this paper, we propose a self-supervised learning (SSL)
approach to learn the spatial anatomical representations from the frames of
magnetic resonance (MR) video clips for the diagnosis of knee medical
conditions. The pretext model learns meaningful spatial context-invariant
representations. The downstream task in our paper is a class imbalanced
multi-label classification. Different experiments show that the features learnt
by the pretext model provide competitive performance in the downstream task.
Moreover, the efficiency and reliability of the proposed pretext model in
learning representations of minority classes without applying any strategy
towards imbalance in the dataset can be seen from the results. To the best of
our knowledge, this work is the first work of its kind in showing the
effectiveness and reliability of self-supervised learning algorithms in class
imbalanced multi-label classification tasks on MR videos.
  The code for evaluation of the proposed work is available at
https://github.com/sadimanna/skid.
