Zhu Yaoyao, Cai Xiuding, Wang Xueyao, Chen Xiaoqing, Fu Zhongliang, Yao Yu
Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu 610213, China.
The School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 101408, China.
Sensors (Basel). 2024 Nov 25;24(23):7511. doi: 10.3390/s24237511.
Data augmentation is a crucial regularization technique for deep neural networks, particularly in medical imaging tasks with limited data. Deep learning models are highly effective at linearizing features, enabling the alteration of feature semantics through the shifting of latent space representations-an approach known as semantic data augmentation (SDA). The paradigm of SDA involves shifting features in a specified direction. Current SDA methods typically sample the amount of shifting from a Gaussian distribution or the sample variance. However, excessive shifting can lead to changes in data labels, which may negatively impact model performance. To address this issue, we propose a computationally efficient method called Bayesian Random Semantic Data Augmentation (BSDA). BSDA can be seamlessly integrated as a plug-and-play component into any neural network. Our experiments demonstrate that BSDA outperforms competitive methods and is suitable for both 2D and 3D medical image datasets, as well as most medical imaging modalities. Additionally, BSDA is compatible with mainstream neural network models and enhances baseline performance. The code is available online.
数据增强是深度神经网络的一种关键正则化技术，特别是在数据有限的医学成像任务中。深度学习模型在特征线性化方面非常有效，能够通过潜在空间表示的移位来改变特征语义——这种方法被称为语义数据增强（SDA）。SDA范式涉及在指定方向上移位特征。当前的SDA方法通常从高斯分布或样本方差中采样移位量。然而，过度移位可能导致数据标签的变化，这可能对模型性能产生负面影响。为了解决这个问题，我们提出了一种计算效率高的方法，称为贝叶斯随机语义数据增强（BSDA）。BSDA可以作为即插即用组件无缝集成到任何神经网络中。我们的实验表明，BSDA优于竞争方法，适用于2D和3D医学图像数据集以及大多数医学成像模态。此外，BSDA与主流神经网络模型兼容，并提高了基线性能。代码可在线获取。
