DiffMix: Diffusion Model-based Data Synthesis for Nuclei Segmentation
  and Classification in Imbalanced Pathology Image Datasets
Hyun-Jic Oh Won-Ki Jeong
Nuclei segmentation and classification is a significant process in pathology
image analysis. Deep learning-based approaches have greatly contributed to the
higher accuracy of this task. However, those approaches suffer from the
imbalanced nuclei data composition, which shows lower classification
performance on the rare nuclei class. In this paper, we propose a realistic
data synthesis method using a diffusion model. We generate two types of virtual
patches to enlarge the training data distribution, which is for balancing the
nuclei class variance and for enlarging the chance to look at various nuclei.
After that, we use a semantic-label-conditioned diffusion model to generate
realistic and high-quality image samples. We demonstrate the efficacy of our
method by experiment results on two imbalanced nuclei datasets, improving the
state-of-the-art networks. The experimental results suggest that the proposed
method improves the classification performance of the rare type nuclei
classification, while showing superior segmentation and classification
performance in imbalanced pathology nuclei datasets.