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.

翻译：医学图像分析应用严重缺乏医学专家适当附加说明的大量数据。 受监督的学习算法需要大量均衡的数据来学习稳健的演示。 通常受监督的学习算法需要各种处理不平衡数据的技术。 另一方面,自监督的学习算法对数据不平衡十分活跃,并且能够学习稳健的演示。 在这项工作中,我们用梯度积累技术培训了3D BYOL自我监督模型,处理一组样本中的大量样本,通常需要自监督算法的样本。 据我们所知,这项工作是这一领域第一组样本中的第一个。我们比较了在ABL眼损伤探测下游任务中通过实验获得的结果,与当代自监督的训练前方法以及由Enuticatics-400预训练前重量制成的ResNet3D-18。 从下游任务实验来看,拟议的框架显然超过了现有基线。