Prompt-Based Grouping Transformer for Nucleus Detection and Classification

Huang, Junjia; Li, Haofeng; Sun, Weijun; Wan, Xiang; Li, Guanbin

doi:10.1007/978-3-031-43993-3_55

Cited by 4 publications

(1 citation statement)

References 26 publications

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“…In practical applications, the procedures such as cell counting (Fridman et al 2012), tumor grading (Fleming et al 2012) and computer-aided diagnosis (CAD) (Saha, Mukherjee, and Chakraborty 2016) all require to identify nuclei as a fundamental task. Some studies (Abousamra et al 2021;Huang et al 2023b) aim to identify both the location and type of cells, while some other works (Stringer et al 2021;Lou et al 2022Lou et al , 2023bMa et al 2023;Yu et al 2023) attempt to determine the nucleus boundaries. However, due Figure 1: The illustration of universal multi-dataset cell nucleus classification.…”

Section: Introductionmentioning

confidence: 99%

UniCell: Universal Cell Nucleus Classification via Prompt Learning

Huang,

Li,

Wan

et al. 2024

AAAI

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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

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