用空间特征引导扩散模型生成真实细胞布局，提升病理图像检测性能。
Spatial Diffusion for Cell Layout Generation
- 基于细胞空间特征设计新型扩散模型生成细胞布局
- 生成布局可指导高质量病理图像合成，显著提升检测精度
- 适合医学图像生成与细胞检测研究者使用
生成模型如GAN和扩散模型已被用于数据增强以提升各类任务表现。本文聚焦于细胞检测中的生成模型应用，即在病理图像中定位并分类细胞。一个重要却被忽视的信息是细胞的空间分布模式。为此，本文提出一种基于空间模式引导的细胞布局生成方法，设计了一种由空间特征引导的新型扩散模型，生成逼真的细胞布局。我们探索了多种密度模型作为空间特征输入扩散模型。在下游任务中，生成的细胞布局可用于指导高质量病理图像的生成，通过引入这些图像进行数据增强，显著提升了当前最优细胞检测方法的性能。代码已开源：https://github.com/superlc1995/Diffusion-cell。
原文摘要 · Abstract (English)
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.
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