  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.
