Abstract
Weakly supervised semantic segmentation offers a label-efficient solution to train segmentation models for volumetric medical imaging. However, existing approaches often rely on 2D encoders that neglect the inherent volumetric nature of the data. We propose TranSamba, a hybrid Transformer-Mamba architecture designed to capture 3D context for weakly supervised volumetric medical segmentation. TranSamba augments a standard Vision Transformer backbone with Cross-Plane Mamba blocks, which leverage the linear complexity of state space models for efficient information exchange across neighboring slices. The information exchange enhances the pairwise self-attention within slices computed by the Transformer blocks, directly contributing to the attention maps for object localization. TranSamba achieves effective volumetric modeling with time complexity that scales linearly with the input volume depth and maintains constant memory usage for batch processing. Extensive experiments on three datasets demonstrate that TranSamba establishes new state-of-the-art performance, consistently outperforming existing methods across diverse modalities and pathologies. Our source code and trained models are openly accessible at: this https URL.
Abstract (translated)
弱监督语义分割为训练体积医学影像的分割模型提供了一种标签高效的解决方案。然而，现有方法通常依赖于二维编码器，忽视了数据本身的三维特性。我们提出了一种混合Transformer-Mamba架构——TranSamba，专门用于捕捉弱监督体积医学分割中的三维上下文信息。TranSamba在标准视觉变压器主干网络的基础上增加了Cross-Plane Mamba块，利用状态空间模型的线性复杂度来实现相邻切片之间高效的信息交换。这种信息交换增强了由Transformer块计算出的片内成对自注意力机制，直接贡献于目标定位的注意图中。 TranSamba能够有效地建模体积数据，并且时间复杂度随着输入体深度呈线性增长，同时保持了批量处理中的常量内存使用率。在三个数据集上的广泛实验表明，TranSamba建立了新的最先进技术性能，在不同模式和病理条件下始终优于现有方法。 我们的源代码和训练模型可在以下网址公开访问：[此处插入实际URL]。
URL
https://arxiv.org/abs/2512.10353
