CellMamba: Adaptive Mamba for Accurate and Efficient Cell Detection


Ruochen Liu (University of Liverpool), Yi.Tian (National University of Singapore), Jiahao Wang (Xi'an Jiaotong-Liverpool University), Hongbin Liu (Xi'an Jiaotong-Liverpool University), Xianxu Hou (Xi'an Jiaotong-Liverpool University), Jingxin Liu (Xi'an Jiaotong-Liverpool University)
The 35th British Machine Vision Conference
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Abstract

Cell detection in pathological images presents unique challenges due to densely packed objects, subtle inter-class differences, and severe background clutter. In this paper, we propose CellMamba, a lightweight and accurate one-stage detector tailored for fine-grained biomedical instance detection. Built upon a VSSD backbone, CellMamba integrates CellMamba Blocks, which couple either NC-Mamba or Multi-Head Self-Attention (MSA) with a novel Triple-Mapping Adaptive Coupling (TMAC) module. TMAC enhances spatial discriminability by splitting channels into two parallel branches, equipped with dual idiosyncratic and one consensus attention map, adaptively fused to preserve local sensitivity and global consistency. Furthermore, we design an Adaptive Mamba Head that fuses multi-scale features via learnable weights for robust detection under varying object sizes. Extensive experiments on two public datasets—CoNSeP and CytoDArk0—demonstrate that CellMamba outperforms both CNN-based, Transformer-based, and Mamba-based baselines in accuracy, while significantly reducing model size and inference latency. Our results validate CellMamba as an efficient and effective solution for high-resolution cell detection.

Citation

@inproceedings{Liu_2025_BMVC,
author    = {Ruochen Liu and Yi.Tian and Jiahao Wang and Hongbin Liu and Xianxu Hou and Jingxin Liu},
title     = {CellMamba: Adaptive Mamba for Accurate and Efficient Cell Detection},
booktitle = {36th British Machine Vision Conference 2025, {BMVC} 2025, Sheffield, UK, November 24-27, 2025},
publisher = {BMVA},
year      = {2025},
url       = {https://bmva-archive.org.uk/bmvc/2025/assets/papers/Paper_173/paper.pdf}
}


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