bibtext: @InProceedings{ Li_Spatial_MICCAI2024,
                   author = { Li, Chen and Hu, Xiaoling and Abousamra, Shahira and Xu, Meilong and Chen, Chao },
                   title = { { Spatial Diffusion for Cell Layout Generation } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15004 },
                   month = {October},
                   pages = { pending },
                }
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.
title: Spatial Diffusion for Cell Layout Generation
authors: Li, Chen Hu, Xiaoling Abousamra, Shahira Xu, Meilong Chen, Chao