Spatial Diffusion for Cell Layout Generation

TL;DR

This paper introduces a novel diffusion model guided by spatial cell patterns to generate realistic cell layouts, which enhances pathology image synthesis and improves cell detection accuracy.

Contribution

The paper presents a new spatial-pattern-guided diffusion model for cell layout generation, addressing overlooked spatial information in existing generative approaches.

Findings

Generated cell layouts improve pathology image quality.

Augmentation with synthetic images boosts cell detection performance.

Spatial features effectively guide realistic cell layout synthesis.

Abstract

Generative models, such as GANs and diffusion models, have been used to augment training sets and boost performances in different tasks. We focus on generative models for cell detection instead, i.e., locating and classifying cells in given pathology images. One important information that has been largely overlooked is the spatial patterns of the cells. In this paper, we propose a spatial-pattern-guided generative model for cell layout generation. Specifically, a novel diffusion model guided by spatial features and generates realistic cell layouts has been proposed. We explore different density models as spatial features for the diffusion model. In downstream tasks, we show that the generated cell layouts can be used to guide the generation of high-quality pathology images. Augmenting with these images can significantly boost the performance of SOTA cell detection methods. The code is available at https://github.com/superlc1995/Diffusion-cell.