Abstract
Self-supervised contrastive learning is among the recent representation learning methods that have shown performance gains in several downstream tasks including semantic segmentation. This paper evaluates strong data augmentation, one of the most important components for self-supervised contrastive learning's improved performance. Strong data augmentation involves applying the composition of multiple augmentation techniques on images. Surprisingly, we find that the existing data augmentations do not always improve performance for semantic segmentation for medical images. We experiment with other augmentations that provide improved performance.
Abstract (translated)
自监督对比学习是最近出现的表示学习方法之一，已在包括语义分割在内的多个下游任务中表现出性能提升。本文评估了强数据增强技术，这是自监督对比学习改进性能的关键组成部分之一。强数据增强涉及到对图像应用多种数据增强技术的组合。令人惊讶的是，我们发现现有的数据增强技术并不总是能够提高医学图像语义分割的性能。我们在实验中尝试了其他一些能提供更好效果的数据增强方法。
URL
https://arxiv.org/abs/2512.05992
