论文标题
样式增强改善医疗图像细分
Style Augmentation improves Medical Image Segmentation
论文作者
论文摘要
由于可用标记的数据的限制，医疗图像分割是深度学习的挑战。传统的数据增强技术已被证明可以通过优化少数培训示例的使用来改善细分网络性能。但是，当前的分割方法不能应对多项研究中观察到的卷积神经网络的强质量偏见。这项工作在Monuseg数据集上显示了已经用于分类任务中的样式增强作用，有助于减少纹理过度拟合并改善细分性能。
Due to the limitation of available labeled data, medical image segmentation is a challenging task for deep learning. Traditional data augmentation techniques have been shown to improve segmentation network performances by optimizing the usage of few training examples. However, current augmentation approaches for segmentation do not tackle the strong texture bias of convolutional neural networks, observed in several studies. This work shows on the MoNuSeg dataset that style augmentation, which is already used in classification tasks, helps reducing texture over-fitting and improves segmentation performance.
