# Category Prompt Mamba Network for Nuclei Segmentation and Classification

arXiv: https://arxiv.org/abs/2503.10422 · HTML: https://mlsanity.com/paper/2503.10422/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Mamba for medical image analysis, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 18 of its 43 references that are in the map.
- First posted on arXiv 13 Mar 2025.

## Placed in

- State-space sequence models (S4, Mamba) / [Mamba for medical image analysis](https://mlsanity.com/ssm/mamba-for-medical-image-analysis/)

## Builds on (most cited first, 18 of 18)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Panoptic Segmentation](https://mlsanity.com/paper/1801.00868/) (2018)
- [HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images](https://mlsanity.com/paper/1812.06499/) (2018)
- [PlainMamba: Improving Non-Hierarchical Mamba in Visual Recognition](https://mlsanity.com/paper/2403.17695/) (2024)
- [SurgicalSAM: Efficient Class Promptable Surgical Instrument Segmentation](https://mlsanity.com/paper/2308.08746/) (2023)
- [VmambaIR: Visual State Space Model for Image Restoration](https://mlsanity.com/paper/2403.11423/) (2024)
- [DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation](https://mlsanity.com/paper/1604.02677/) (2016)
- [Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images](https://mlsanity.com/paper/2004.03037/) (2020)
- [Multi-scale fully convolutional neural networks for histopathology image segmentation: from nuclear aberrations to the global tissue architecture](https://mlsanity.com/paper/1909.10726/) (2019)
- [DoNet: Deep De-overlapping Network for Cytology Instance Segmentation](https://mlsanity.com/paper/2303.14373/) (2023)
- [TransNuSeg: A Lightweight Multi-Task Transformer for Nuclei Segmentation](https://mlsanity.com/paper/2307.08051/) (2023)
- [Structure Embedded Nucleus Classification for Histopathology Images](https://mlsanity.com/paper/2302.11416/) (2023)
- [Cell Graph Transformer for Nuclei Classification](https://mlsanity.com/paper/2402.12946/) (2024)
- [DAWN: Domain-Adaptive Weakly Supervised Nuclei Segmentation via Cross-Task Interactions](https://mlsanity.com/paper/2404.14956/) (2024)
- [GradMix for nuclei segmentation and classification in imbalanced pathology image datasets](https://mlsanity.com/paper/2210.12938/) (2022)
- [Dynamic Pseudo Label Optimization in Point-Supervised Nuclei Segmentation](https://mlsanity.com/paper/2406.16427/) (2024)

## Built on by (most cited first, 3 of 3)

- [The Four Color Theorem for Cell Instance Segmentation](https://mlsanity.com/paper/2506.09724/) (2025)
- [Disco: Densely-overlapping Cell Instance Segmentation via Adjacency-aware Collaborative Coloring](https://mlsanity.com/paper/2602.05420/) (2026)
- [Prompt Mechanisms in Medical Imaging: A Comprehensive Survey](https://mlsanity.com/paper/2507.01055/) (2025)

## Abstract (arXiv)

Nuclei segmentation and classification provide an essential basis for tumor immune microenvironment analysis. The previous nuclei segmentation and classification models require splitting large images into smaller patches for training, leading to two significant issues. First, nuclei at the borders of adjacent patches often misalign during inference. Second, this patch-based approach significantly increases the model's training and inference time. Recently, Mamba has garnered attention for its ability to model large-scale images with linear time complexity and low memory consumption. It offers a promising solution for training nuclei segmentation and classification models on full-sized images. However, the Mamba orientation-based scanning method lacks account for category-specific features, resulting in sub-optimal performance in scenarios with imbalanced class distributions. To address these challenges, this paper introduces a novel scanning strategy based on category probability sorting, which independently ranks and scans features for each category according to confidence from high to low. This approach enhances the feature representation of uncertain samples and mitigates the issues caused by imbalanced distributions. Extensive experiments conducted on four public datasets demonstrate that our method outperforms state-of-the-art approaches, delivering superior performance in nuclei segmentation and classification tasks.