论文标题
核分割和分类的毕业词不平衡的病理图像数据集
GradMix for nuclei segmentation and classification in imbalanced pathology image datasets
论文作者
论文摘要
核的自动分割和分类是数字病理学中的重要任务。当前基于深度学习的方法需要病理学家大量注释的数据集。但是，现有的数据集通常在不同类型的核之间存在不平衡，从而导致大量性能降解。在本文中，我们提出了一种简单但有效的数据增强技术，称为GradMix，该技术专为核分割和分类而设计。 GradMix采用一对主要级别的核和稀有级核，创建定制的混合面具，并使用面具将它们结合起来，以生成新的稀有级核。当它结合了两个核时，GradMix通过使用定制的混合面膜来考虑核和相邻环境。这使我们能够在不同的环境中生成逼真的稀有级核。我们采用了两个数据集来评估GradMix的有效性。实验结果表明，GradMix能够改善核分割和分类不平衡病理图像数据集的性能。
An automated segmentation and classification of nuclei is an essential task in digital pathology. The current deep learning-based approaches require a vast amount of annotated datasets by pathologists. However, the existing datasets are imbalanced among different types of nuclei in general, leading to a substantial performance degradation. In this paper, we propose a simple but effective data augmentation technique, termed GradMix, that is specifically designed for nuclei segmentation and classification. GradMix takes a pair of a major-class nucleus and a rare-class nucleus, creates a customized mixing mask, and combines them using the mask to generate a new rare-class nucleus. As it combines two nuclei, GradMix considers both nuclei and the neighboring environment by using the customized mixing mask. This allows us to generate realistic rare-class nuclei with varying environments. We employed two datasets to evaluate the effectiveness of GradMix. The experimental results suggest that GradMix is able to improve the performance of nuclei segmentation and classification in imbalanced pathology image datasets.
