医学图像分割中，强数据增强未必有效，新方法反而提升性能
Stronger is not better: Better Augmentations in Contrastive Learning for Medical Image Segmentation
- 测试多种强增强组合，发现并非所有都提升分割效果
- 提出新增强策略，在多个医疗数据集上显著提高分割精度
- 适合关注医学图像自监督学习的科研与临床开发者
自监督对比学习是近年来在语义分割等下游任务中表现优异的表征学习方法。本文评估了强数据增强——对比学习中至关重要的组成部分——对医学图像语义分割的影响。强数据增强指对图像施加多种增强技术的复合操作。令人意外的是，现有增强策略在医学图像分割中并不总能提升性能。我们尝试了其他增强方式，发现部分新方案能显著改善分割结果，在多个公开医疗数据集上验证了有效性。
原文摘要 · Abstract (English)
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.
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