发表机构
Ankara Medipol University; University of Galway（安卡拉梅迪波尔大学; 戈尔韦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对SSM在医学图像中难以区分病理与正常解剖的问题，提出非对称双分支SCDM，利用排斥门和差分推理实现表示解缠，在RSNA肺炎数据集上以更少参数和计算量达到0.858的AUC。
AI 中文摘要
状态空间模型（SSMs），特别是VMamba，已成为医学图像分析中建模长距离依赖的高效替代方案。然而，将细微的病理特征与视觉上相似的解剖背景区分开来仍然是一个重大挑战。现有的SSM架构通常学习纠缠的表示，缺乏将疾病特异性信号与正常解剖结构分离的显式机制。为解决这一局限，我们提出了空间-上下文差分Mamba（SCDM），一种用于选择性表示解缠的非对称双分支架构。SCDM引入了一个用于提取判别性特征的正分支和一个主动建模并抑制正常解剖上下文的负分支。这种分离通过一个相似性驱动的排斥门和差分推理规则实现，该规则促进竞争性特征学习，而无需额外的分支标签或增加模型容量。在RSNA肺炎数据集上的评估中，SCDM实现了具有竞争力的分类性能（AUC为0.858），同时相比标准VMamba和视觉Transformer基线，所需参数（29.4M）和FLOPs（1.44G）显著更少。此外，激活分析表明，我们的差分机制产生了高度精确的定位，通过抑制不相关的解剖干扰物有效地隔离病灶。
英文摘要
State Space Models (SSMs), particularly VMamba, have emerged as efficient alternatives for modeling long-range dependencies in medical image analysis. However, distinguishing subtle pathological features from visually similar anatomical backgrounds remains a significant challenge. Existing SSM architectures often learn entangled representations, lacking explicit mechanisms to separate disease-specific signals from normal anatomy. To address this limitation, we propose Spatial-Contextual Differential Mamba (SCDM), an asymmetric dual-branch architecture designed for selective representational disentanglement. SCDM introduces a Positive Branch for extracting discriminative features and a Negative Branch that actively models and suppresses normal anatomical context. This separation is achieved through a similarity-driven repulsion gate and a differential inference rule, which promote competitive feature learning without requiring additional branch labels or increasing model capacity. Evaluated on the RSNA Pneumonia dataset, SCDM achieves competitive classification performance (AUC of 0.858) while requiring significantly fewer parameters (29.4M) and FLOPs (1.44G) compared to standard VMamba and vision transformer baselines. Furthermore, activation analyses demonstrate that our differential mechanism yields highly precise localization, effectively isolating lesions by inhibiting irrelevant anatomical distractors.
Comments9 pages, 5 figures
