Medical image data are often limited due to the expensive acquisition and
annotation process. Hence, training a deep-learning model with only raw data
can easily lead to overfitting. One solution to this problem is to augment the
raw data with various transformations, improving the model's ability to
generalize to new data. However, manually configuring a generic augmentation
combination and parameters for different datasets is non-trivial due to
inconsistent acquisition approaches and data distributions. Therefore,
automatic data augmentation is proposed to learn favorable augmentation
strategies for different datasets while incurring large GPU overhead. To this
end, we present a novel method, called Dynamic Data Augmentation (DDAug), which
is efficient and has negligible computation cost. Our DDAug develops a
hierarchical tree structure to represent various augmentations and utilizes an
efficient Monte-Carlo tree searching algorithm to update, prune, and sample the
tree. As a result, the augmentation pipeline can be optimized for each dataset
automatically. Experiments on multiple Prostate MRI datasets show that our
method outperforms the current state-of-the-art data augmentation strategies.

本研究提出了一种自适应数据增强方法，称为 DDAug，它通过动态建立一棵树状结构，使用高效的蒙特卡罗树搜索算法来更新、修剪和采样该树，从而自动优化数据集的数据增强管道，实验结果表明该方法优于当前最先进的数据增强策略。