  In multi-class histopathology nuclei analysis tasks, the lack of training
data becomes a main bottleneck for the performance of learning-based methods.
To tackle this challenge, previous methods have utilized generative models to
increase data by generating synthetic samples. However, existing methods often
overlook the importance of considering the context of biological tissues (e.g.,
shape, spatial layout, and tissue type) in the synthetic data. Moreover, while
generative models have shown superior performance in synthesizing realistic
histopathology images, none of the existing methods are capable of producing
image-label pairs at the same time. In this paper, we introduce a novel
framework for co-synthesizing histopathology nuclei images and paired semantic
labels using a context-conditioned joint diffusion model. We propose
conditioning of a diffusion model using nucleus centroid layouts with
structure-related text prompts to incorporate spatial and structural context
information into the generation targets. Moreover, we enhance the granularity
of our synthesized semantic labels by generating instance-wise nuclei labels
using distance maps synthesized concurrently in conjunction with the images and
semantic labels. We demonstrate the effectiveness of our framework in
generating high-quality samples on multi-institutional, multi-organ, and
multi-modality datasets. Our synthetic data consistently outperforms existing
augmentation methods in the downstream tasks of nuclei segmentation and
classification.
