  Applications on Medical Image Analysis suffer from acute shortage of large
volume of data properly annotated by medical experts. Supervised Learning
algorithms require a large volumes of balanced data to learn robust
representations. Often supervised learning algorithms require various
techniques to deal with imbalanced data. Self-supervised learning algorithms on
the other hand are robust to imbalance in the data and are capable of learning
robust representations. In this work, we train a 3D BYOL self-supervised model
using gradient accumulation technique to deal with the large number of samples
in a batch generally required in a self-supervised algorithm. To the best of
our knowledge, this work is one of the first of its kind in this domain. We
compare the results obtained through our experiments in the downstream task of
ACL Tear Injury detection with the contemporary self-supervised pre-training
methods and also with ResNet3D-18 initialized with the Kinetics-400 pre-trained
weights. From the downstream task experiments, it is evident that the proposed
framework outperforms the existing baselines.
