  In the field of computational pathology, deep learning algorithms have made
significant progress in tasks such as nuclei segmentation and classification.
However, the potential of these advanced methods is limited by the lack of
available labeled data. Although image synthesis via recent generative models
has been actively explored to address this challenge, existing works have
barely addressed label augmentation and are mostly limited to single-class and
unconditional label generation. In this paper, we introduce a novel two-stage
framework for multi-class nuclei data augmentation using text-conditional
diffusion models. In the first stage, we innovate nuclei label synthesis by
generating multi-class semantic labels and corresponding instance maps through
a joint diffusion model conditioned by text prompts that specify the label
structure information. In the second stage, we utilize a semantic and
text-conditional latent diffusion model to efficiently generate high-quality
pathology images that align with the generated nuclei label images. We
demonstrate the effectiveness of our method on large and diverse pathology
nuclei datasets, with evaluations including qualitative and quantitative
analyses, as well as assessments of downstream tasks.
