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}.

翻译：数据增强是深度神经网络中至关重要的正则化技术，尤其在医学图像分类领域。主流的数据增强方法通常在图像层面实施。由于医学影像的特殊性与多样性，通常需要专业知识来设计有效的增强策略，而不恰当的增强操作可能降低模型性能。尽管存在自动增强方法，但其计算成本高昂。语义数据增强可通过在特征空间中对特征进行平移来实现，然而过度平移可能破坏图像标签。为解决这些问题，我们提出了一种计算高效且无需人工设计的特征级数据增强方法——贝叶斯随机语义数据增强。该方法利用变分贝叶斯估计可增强幅度的概率分布，随后从该分布中采样并叠加至原始特征以执行语义数据增强。我们在九个二维及五个三维医学图像数据集上进行了实验验证。实验结果表明，BSDA的性能优于当前主流数据增强方法。此外，BSDA能够作为即插即用模块便捷地集成至CNN或Transformer架构中，有效提升网络性能。相关代码已发布于：https://github.com/YaoyaoZhu19/BSDA。