GeoSemba：为医学图像分割重构跨范式表征的状态空间模型
GeoSemba: Reconstructing State Space Model for Cross Paradigm Representation in Medical Image Segmentation
把 Mamba 状态空间扫描从单一序列建模扩展为「几何-语义跨层交互 + 空间-通道跨维亲和」的双重精炼，对做医学分割或想把线性复杂度 SSM 用到密集预测的人，SSR 的几何条件区域传播与 CAR 的由粗到细亲和筛选都是可直接迁移的模块设计。
原文依据 jointly models cross-level geometric-semantic interactions and cross-dimensional spatial-channel dependencies within a single scan
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论基于 Mamba 的模型凭借线性复杂度的状态空间建模，已成为医学图像分割中一个有前景的范式。然而，其有效性仍受限于解剖几何与组织特异语义之间的失配，以及空间上相互纠缠的诊断线索。为解决这些局限，我们提出 GeoSemba，一个基于 Mamba 的分割框架，在单次扫描内联合建模跨层级的几何-语义交互与跨维度的空间-通道依赖。为此，GeoSemba 包含两个互补组件。语义引导状态精炼器（SSR）推导具有语义判别性的区域代表，并利用几何条件化的区域间依赖，实现语义在结构相关区域之间的一致传播。跨维亲和精炼器（CAR）采用宏观感知与微观聚焦的由粗到细策略，选择性增强有信息量的空间-通道交互，同时抑制微弱与噪声相关性。在涵盖六种医学影像模态的基准数据集上的大量实验表明，GeoSemba 在保持高计算效率的同时，持续取得更优的分割精度。
英文原文摘要 1265 字符
Mamba-based models have emerged as a promising paradigm for medical image segmentation due to their linear-complexity state-space modeling. However, their effectiveness is still limited by the mismatch between anatomical geometry and tissue-specific semantics, as well as by spatially entangled diagnostic cues. To address these limitations, we propose GeoSemba, a Mamba-based segmentation framework that jointly models cross-level geometric-semantic interactions and cross-dimensional spatial-channel dependencies within a single scan. To this end, GeoSemba comprises two complementary components. The Semantic-guided State Refiner (SSR) derives semantically discriminative region representatives and leverages geometry-conditioned inter-region dependencies to enable coherent semantic propagation across structurally related regions. The Cross-dimensional Affinity Refiner (CAR) adopts a coarse-to-fine strategy of macro-perception and micro-focus to selectively enhance informative spatial-channel interactions while suppressing weak and noisy correlations. Extensive experiments on benchmark datasets spanning six medical imaging modalities show that GeoSemba consistently delivers superior segmentation accuracy while maintaining high computational efficiency.