Data augmentation is a critical regularization technique for deep neural
networks, particularly in medical image classification. Popular data
augmentation approaches include image transformation-based methods, generative
data augmentation, and automatic data augmentation. However, these approaches
encounter notable limitations: image transformation-based and automated data
augmentation techniques cannot implement semantic transformations, leading to a
constrained variety of augmented samples, and generative data augmentation
methods are computationally expensive. In response to these challenges, we
proposed Bayesian Random Semantic Data Augmentation (BRSDA), a novel,
efficient, and plug-and-play semantic data augmentation method. BRSDA is
motivated by a simple translation in the feature space along specific
directions that can effectuate semantic transformations. When given a feature,
we define its augmentable semantic magnitude as a random variable and estimate
its distribution using variational Bayesian, then sample semantic magnitude and
add to the randomly selected semantic direction to achieve semantic data
augmentation. We demonstrate the effectiveness of BRSDA on five 2D and six 3D
medical image datasets covering nine modalities. We also test BRSDA with
mainstream neural network architectures, showcasing its robustness.
Furthermore, combining BRSDA with other leading data augmentation methods
achieves superior performance. Code is available online at
https://github.com/YaoyaoZhu19/BRSDA.

提出了一种名为 Bayesian Random Semantic Data Augmentation 的新型、高效且即插即用的语义数据增强方法（BRSDA），通过在特征空间中沿特定方向进行简单的平移，实现语义转换，并结合其他主流的数据增强方法，取得了优越的性能。