Research Paper#Computer Vision, Biomedical Image Analysis, Deep Learning🔬 ResearchAnalyzed: Jan 4, 2026 00:04
CellMamba: Efficient Cell Detection with Adaptive Mamba
Published:Dec 25, 2025 23:05
•1 min read
•ArXiv
Analysis
This paper introduces CellMamba, a novel one-stage detector for cell detection in pathological images. It addresses the challenges of dense packing, subtle inter-class differences, and background clutter. The core innovation lies in the integration of CellMamba Blocks, which combine Mamba or Multi-Head Self-Attention with a Triple-Mapping Adaptive Coupling (TMAC) module for enhanced spatial discrimination. The Adaptive Mamba Head further improves performance by fusing multi-scale features. The paper's significance lies in its demonstration of superior accuracy, reduced model size, and lower inference latency compared to existing methods, making it a promising solution for high-resolution cell detection.
Key Takeaways
Reference
“CellMamba outperforms both CNN-based, Transformer-based, and Mamba-based baselines in accuracy, while significantly reducing model size and inference latency.”