Prompt-based Grouping Transformer for Nucleus Detection and
  Classification
Junjia Huang Haofeng Li Weijun Sun Xiang Wan Guanbin Li
Automatic nuclei detection and classification can produce effective
information for disease diagnosis. Most existing methods classify nuclei
independently or do not make full use of the semantic similarity between nuclei
and their grouping features. In this paper, we propose a novel end-to-end
nuclei detection and classification framework based on a grouping
transformer-based classifier. The nuclei classifier learns and updates the
representations of nuclei groups and categories via hierarchically grouping the
nucleus embeddings. Then the cell types are predicted with the pairwise
correlations between categorical embeddings and nucleus features. For the
efficiency of the fully transformer-based framework, we take the nucleus group
embeddings as the input prompts of backbone, which helps harvest grouping
guided features by tuning only the prompts instead of the whole backbone.
Experimental results show that the proposed method significantly outperforms
the existing models on three datasets.