This paper presents an effective and general data augmentation framework for
medical image segmentation. We adopt a computationally efficient and
data-efficient gradient-based meta-learning scheme to explicitly align the
distribution of training and validation data which is used as a proxy for
unseen test data. We improve the current data augmentation strategies with two
core designs. First, we learn class-specific training-time data augmentation
(TRA) effectively increasing the heterogeneity within the training subsets and
tackling the class imbalance common in segmentation. Second, we jointly
optimize TRA and test-time data augmentation (TEA), which are closely connected
as both aim to align the training and test data distribution but were so far
considered separately in previous works. We demonstrate the effectiveness of
our method on four medical image segmentation tasks across different scenarios
with two state-of-the-art segmentation models, DeepMedic and nnU-Net. Extensive
experimentation shows that the proposed data augmentation framework can
significantly and consistently improve the segmentation performance when
compared to existing solutions. Code is publicly available.

本文提出了一种针对医学图像分割的高效通用数据增强框架，采用了计算效率高且数据效率高的基于梯度的元学习方案，通过使用用于未见测试数据的代理验证数据明确对齐训练和验证数据的分布以改进增广策略。本文通过两个核心设计改进了当前数据增广策略，即在训练时有效学习类别特定的数据增强并联合优化训练时和测试时数据增强，实验证明该方法针对医学图像分割存在的分类不平衡问题，可显著且一致地提高分割效果。