  Data augmentation is a crucial regularization technique for deep neural
networks, particularly in medical image classification. Mainstream data
augmentation (DA) methods are usually applied at the image level. Due to the
specificity and diversity of medical imaging, expertise is often required to
design effective DA strategies, and improper augmentation operations can
degrade model performance. Although automatic augmentation methods exist, they
are computationally intensive. Semantic data augmentation can implemented by
translating features in feature space. However, over-translation may violate
the image label. To address these issues, we propose \emph{Bayesian Random
Semantic Data Augmentation} (BSDA), a computationally efficient and
handcraft-free feature-level DA method. BSDA uses variational Bayesian to
estimate the distribution of the augmentable magnitudes, and then a sample from
this distribution is added to the original features to perform semantic data
augmentation. We performed experiments on nine 2D and five 3D medical image
datasets. Experimental results show that BSDA outperforms current DA methods.
Additionally, BSDA can be easily assembled into CNNs or Transformers as a
plug-and-play module, improving the network's performance. The code is
available online at \url{https://github.com/YaoyaoZhu19/BSDA}.
