Due to the high cost of annotation or the rarity of some diseases, medical image segmentation is often limited by data scarcity and the resulting overfitting problem. Self-supervised learning and semi-supervised learning can mitigate the data scarcity challenge to some extent. However, both of these paradigms are complex and require either hand-crafted pretexts or well-defined pseudo-labels. In contrast, data augmentation represents a relatively simple and straightforward approach to addressing data scarcity issues. It has led to significant improvements in image recognition tasks. However, the effectiveness of local image editing augmentation techniques in the context of segmentation has been less explored. Additionally, traditional data augmentation methods for local image editing augmentation methods generally utilize square regions, which cause a loss of contour information.

中文翻译：

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HSMix：用于医学图像分割的硬混合和软混合数据增强


由于标注成本高或某些疾病罕见，医学图像分割往往受到数据稀缺和由此产生的过拟合问题的限制。自监督学习和半监督学习可以在一定程度上缓解数据稀缺的挑战。然而，这两种范式都很复杂，需要手工制作的借口或定义明确的伪标签。相比之下，数据增强代表了解决数据稀缺问题的一种相对简单明了的方法。它导致了图像识别任务的显著改进。然而，局部图像编辑增强技术在分割背景下的有效性尚未得到充分探索。此外，用于局部图像编辑增强方法的传统数据增强方法通常使用方形区域，这会导致等值线信息的丢失。