  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
