PanopMamba：用于核全景分割的视觉状态空间建模
PanopMamba: Vision State Space Modeling for Nuclei Panoptic Segmentation
AI总结：
PanopMamba通过结合Mamba和Transformer，提出了一种新的混合编码器-解码器架构，用于解决核全景分割中的小物体检测、模糊边界处理和类别不平衡问题。
摘要：
核全景分割通过整合不同细胞类型的语义分割和实例分割，支持癌症诊断，以分析组织结构和病理图像中的单个核。主要挑战包括检测小物体、处理模糊边界以及解决类别不平衡问题。为了解决这些问题，我们提出了PanopMamba，一种新颖的混合编码器-解码器架构，整合了Mamba和Transformer，并通过状态空间建模进行额外的特征增强融合。我们设计了多尺度Mamba主干网络和基于状态空间模型（SSM）的融合网络，以在金字塔特征中实现高效的长距离感知，从而扩展纯编码器-解码器框架，同时促进核多尺度特征之间的信息共享。所提出的基于SSM的特征增强融合整合了金字塔特征网络和不同空间尺度的动态特征增强，增强了密集重叠核在语义和空间维度上的特征表示。据我们所知，这是首个基于Mamba的全景分割方法。此外，我们引入了替代的评估指标，包括图像级全景质量（iPQ）、边界加权PQ（wPQ）和频率加权PQ（fwPQ），这些指标专门设计用于解决核分割的特殊挑战，从而缓解传统PQ中可能存在的偏差。在两个多类核分割基准数据集MoNuSAC2020和NuInsSeg上的实验评估表明，PanopMamba在核全景分割方面优于最先进的方法。因此，PanopMamba的鲁棒性在各种指标上得到验证，同时PQ变体的独特性也得到展示。代码可在https://github.com/mkang315/PanopMamba上获取。
英文摘要：
Nuclei panoptic segmentation supports cancer diagnostics by integrating both semantic and instance segmentation of different cell types to analyze overall tissue structure and individual nuclei in histopathology images. Major challenges include detecting small objects, handling ambiguous boundaries, and addressing class imbalance. To address these issues, we propose PanopMamba, a novel hybrid encoder-decoder architecture that integrates Mamba and Transformer with additional feature-enhanced fusion via state space modeling. We design a multiscale Mamba backbone and a State Space Model (SSM)-based fusion network to enable efficient long-range perception in pyramid features, thereby extending the pure encoder-decoder framework while facilitating information sharing across multiscale features of nuclei. The proposed SSM-based feature-enhanced fusion integrates pyramid feature networks and dynamic feature enhancement across different spatial scales, enhancing the feature representation of densely overlapping nuclei in both semantic and spatial dimensions. To the best of our knowledge, this is the first Mamba-based approach for panoptic segmentation. Additionally, we introduce alternative evaluation metrics, including image-level Panoptic Quality ($i$PQ), boundary-weighted PQ ($w$PQ), and frequency-weighted PQ ($fw$PQ), which are specifically designed to address the unique challenges of nuclei segmentation and thereby mitigate the potential bias inherent in vanilla PQ. Experimental evaluations on two multiclass nuclei segmentation benchmark datasets, MoNuSAC2020 and NuInsSeg, demonstrate the superiority of PanopMamba for nuclei panoptic segmentation over state-of-the-art methods. Consequently, the robustness of PanopMamba is validated across various metrics, while the distinctiveness of PQ variants is also demonstrated. Code is available at https://github.com/mkang315/PanopMamba.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
