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.

翻译：在医学图像分析中,获取高质量数据的费用和专家对这些数据的说明是许多医疗应用中的一个障碍。使用的技术大多以监督学习框架为基础,需要大量附加说明的数据才能取得令人满意的业绩。作为替代办法,我们在本文件中提议采用自监督学习方法,从磁共振(MR)视频剪辑框架中学习空间解剖表解,以诊断膝盖病情。借口模型学习了有意义的空间环境差异表征。我们文件中的下游任务是阶级不平衡的多标签分类。不同的实验显示,通过借口模型所学的特征在下游任务中具有竞争性。此外,在学习少数群体课程时,拟议的借口模型的效率和可靠性从结果中可以看出,没有采用任何战略来纠正数据集的不平衡。据我们所知,这项工作是同类工作的第一件工作,即显示在MR视频上显示课堂不平衡的多标签分类任务中自我监督的学习算法的有效性和可靠性。拟议的工作评价代码可在 https://giast/combs。