Self-supervised learning (SSL) approaches have achieved great success when the amount of labeled data is limited. Within SSL, models learn robust feature representations by solving pretext tasks. One such pretext task is contrastive learning, which involves forming pairs of similar and dissimilar input samples, guiding the model to distinguish between them. In this work, we investigate the application of contrastive learning to the domain of medical image analysis. Our findings reveal that MoCo v2, a state-of-the-art contrastive learning method, encounters dimensional collapse when applied to medical images. This is attributed to the high degree of inter-image similarity shared between the medical images. To address this, we propose two key contributions: local feature learning and feature decorrelation. Local feature learning improves the ability of the model to focus on the local regions of the image, while feature decorrelation removes the linear dependence among the features. Our experimental findings demonstrate that our contributions significantly enhance the model's performance in the downstream task of medical segmentation, both in the linear evaluation and full fine-tuning settings. This work illustrates the importance of effectively adapting SSL techniques to the characteristics of medical imaging tasks. The source code will be made publicly available at: https://github.com/CAMMA-public/med-moco

翻译：自监督学习（SSL）方法在标注数据有限的情况下取得了巨大成功。在SSL中，模型通过解决前置任务来学习稳健的特征表示。其中一种前置任务是对比学习，它通过构建相似和不相似输入样本对，引导模型区分它们。本研究探讨了对比学习在医学图像分析领域的应用。我们的发现表明，最先进的对比学习方法MoCo v2在处理医学图像时遭遇了维度坍塌现象，其原因在于医学图像之间存在高度图像间相似性。针对这一问题，我们提出了两项关键贡献：局部特征学习与特征去相关。局部特征学习提升了模型聚焦图像局部区域的能力，而特征去相关则消除了特征之间的线性依赖。实验结果证明，我们的贡献显著增强了模型在下游医学分割任务中的性能，无论是在线性评估还是完全微调设置下。本研究阐明了有效将SSL技术适应医学成像任务特性的重要性。源代码将公开发布于：https://github.com/CAMMA-public/med-moco