  The success of deep learning based models for computer vision applications
requires large scale human annotated data which are often expensive to
generate. Self-supervised learning, a subset of unsupervised learning, handles
this problem by learning meaningful features from unlabeled image or video
data. In this paper, we propose a self-supervised learning approach to learn
transferable features from MR video clips by enforcing the model to learn
anatomical features. The pretext task models are designed to predict the
correct ordering of the jumbled image patches that the MR video frames are
divided into. To the best of our knowledge, none of the supervised learning
models performing injury classification task from MR video provide any
explanation for the decisions made by the models and hence makes our work the
first of its kind on MR video data. Experiments on the pretext task show that
this proposed approach enables the model to learn spatial context invariant
features which help for reliable and explainable performance in downstream
tasks like classification of Anterior Cruciate Ligament tear injury from knee
MRI. The efficiency of the novel Convolutional Neural Network proposed in this
paper is reflected in the experimental results obtained in the downstream task.
