Energy-Mamba：用于医学图像分类的物理约束状态空间模型
Energy-Mamba: A Physics-Constrained State-Space Model for Medical Image Classification
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中文总结 AI 辅助
Energy-Mamba将物理约束融入Mamba状态空间模型，解决医学图像分类的表征漂移问题，在四个医学图像数据集上以更少参数实现了最优分类性能。
中文摘要 AI 辅助
状态空间模型（SSMs），尤其是Mamba，具备针对长程依赖的线性时间复杂度，使其在标注数据有限的医学成像领域颇具吸引力。然而，将这些序列模型通过无约束的状态演化适配到二维图像时会引发表征漂移，即动态隐状态会逐渐丢失对局部图像特征的保真度。我们提出Energy-Mamba，通过可学习的势能函数将SSM动态与物理信息约束相融合，该函数可量化演化状态与静态局部特征之间的兼容性。我们的Energy-Mamba模块引入了基于梯度的强制项，该项通过自动微分动态计算，能将状态拉向低能量配置以维持局部视觉保真度。此公式借鉴了哈密顿动力学：动能（SSM扫描）与势能（我们的约束函数）共同调控状态轨迹。这种架构先验可学习到用于构建鲁棒、保真表征的隐式约束，这对医学成像至关重要，因为细粒度的局部细节是准确诊断的关键。在四个数据集（视网膜OCT、胸部X射线、显微镜图像、腹部CT）上进行评估，Energy-Mamba以显著更少的参数实现了最先进的分类性能，表明物理信息的基础可提升医学视觉任务的效率与表征质量。
英文摘要
State-Space Models (SSMs), particularly Mamba, offer linear-time complexity for long-range dependencies, making them attractive for medical imaging with limited annotated data. However, adapting these sequential models to 2D images through unconstrained state evolution causes representational drift, the dynamic hidden state progressively loses fidelity to local image features. We introduce Energy-Mamba, integrating SSM dynamics with physics-informed constraints via a learnable potential energy function that quantifies compatibility between evolving states and static local features. Our Energy-Mamba Block introduces a gradient-based forcing term, computed dynamically via automatic differentiation, that pulls states toward low-energy configurations maintaining local visual fidelity. This formulation mirrors Hamiltonian dynamics: kinetic energy (SSM scan) plus potential energy (our constraint function) govern state trajectories. This architectural prior enables learning implicit constraints for robust, faithful representations, crucial in medical imaging where fine-grained local detail drives accurate diagnosis. Evaluated on four datasets (retinal OCT, chest X-ray, microscopy, abdominal CT), Energy-Mamba achieves state-of-the-art classification performance with significantly fewer parameters, demonstrating that physics-informed grounding can enhance both efficiency and representational quality in medical vision tasks.
