The recognition of multi-class cell nuclei can significantly facilitate the process of histopathological diagnosis. Numerous pathological datasets are currently available, but their annotations are inconsistent. Most existing methods require individual training on each dataset to deduce the relevant labels and lack the use of common knowledge across datasets, consequently restricting the quality of recognition. In this paper, we propose a universal cell nucleus classification framework (UniCell), which employs a novel prompt learning mechanism to uniformly predict the corresponding categories of pathological images from different dataset domains. In particular, our framework adopts an end-to-end architecture for nuclei detection and classification, and utilizes flexible prediction heads for adapting various datasets. Moreover, we develop a Dynamic Prompt Module (DPM) that exploits the properties of multiple datasets to enhance features. The DPM first integrates the embeddings of datasets and semantic categories, and then employs the integrated prompts to refine image representations, efficiently harvesting the shared knowledge among the related cell types and data sources. Experimental results demonstrate that the proposed method effectively achieves the state-of-the-art results on four nucleus detection and classification benchmarks. Code and models are available at https://github.com/lhaof/UniCell

翻译：多类细胞核的识别可显著促进组织病理学诊断进程。当前存在大量病理数据集，但其标注标准不一致。现有方法大多需要在每个数据集上单独训练以推断相关标签，缺乏跨数据集的通用知识利用，因而限制了识别质量。本文提出一种通用细胞核分类框架UniCell，该框架采用新型提示学习机制，可统一预测来自不同数据集域的病理图像对应类别。具体而言，本框架采用端到端架构实现细胞核检测与分类，并利用灵活预测头适配不同数据集。此外，我们开发了动态提示模块DPM，该模块通过挖掘多数据集特性增强特征表达：首先整合数据集嵌入与语义类别嵌入，继而利用整合提示优化图像表征，高效提取相关细胞类型与数据源间的共享知识。实验结果表明，该方法在四个细胞核检测与分类基准上均取得最优结果。代码与模型已开源至https://github.com/lhaof/UniCell。