Spatial Diffusion for Cell Layout Generation
This work addresses the need for better generative models in medical imaging, specifically for cell detection, by incorporating spatial patterns, though it appears incremental as it builds on existing diffusion models.
The paper tackles the problem of generating realistic cell layouts for pathology images by proposing a spatial-pattern-guided diffusion model, which improves cell detection performance when used for data augmentation.
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.