论文标题
3D医疗图像细分的自动数据增强
Automatic Data Augmentation for 3D Medical Image Segmentation
论文作者
论文摘要
数据增强是改善深神经网络泛化性能的有效通用技术。它可以丰富在医学图像分割任务中必不可少的培训样本的多样性，因为1）医疗图像数据集的规模通常较小，这可能会增加过度拟合的风险； 2）不同对象（例如器官或肿瘤）的形状和方式是唯一的，因此需要定制的数据增强策略。但是，大多数数据增强实现都是手工制作的，并且在医学图像处理中是次优的。为了充分利用数据增强的潜力，我们提出了一种有效的算法来自动搜索最佳的增强策略。我们制定了耦合优化W.R.T.通过随机放松，网络权重和增强参数分为可区分的形式。这种公式使我们能够应用基于梯度的替代方法来求解它，即具有自适应阶梯尺寸的随机天然梯度方法。据我们所知，这是医疗图像分割任务中首次采用可区分自动数据扩展。我们的数值实验表明，所提出的方法极大地胜过现有的最先进模型的建筑数据增强。
Data augmentation is an effective and universal technique for improving generalization performance of deep neural networks. It could enrich diversity of training samples that is essential in medical image segmentation tasks because 1) the scale of medical image dataset is typically smaller, which may increase the risk of overfitting; 2) the shape and modality of different objects such as organs or tumors are unique, thus requiring customized data augmentation policy. However, most data augmentation implementations are hand-crafted and suboptimal in medical image processing. To fully exploit the potential of data augmentation, we propose an efficient algorithm to automatically search for the optimal augmentation strategies. We formulate the coupled optimization w.r.t. network weights and augmentation parameters into a differentiable form by means of stochastic relaxation. This formulation allows us to apply alternative gradient-based methods to solve it, i.e. stochastic natural gradient method with adaptive step-size. To the best of our knowledge, it is the first time that differentiable automatic data augmentation is employed in medical image segmentation tasks. Our numerical experiments demonstrate that the proposed approach significantly outperforms existing build-in data augmentation of state-of-the-art models.
