PolypFlow：利用流驱动动力学强化息肉分割
PolypFlow: Reinforcing Polyp Segmentation with Flow-Driven Dynamics
AI总结：
针对息肉分割中形态不规则、边界模糊及成像条件异质的问题，提出流匹配增强架构PolypFLow，通过求解ODE沿学习速度场逐步细化分割，实现可解释优化与边界感知鲁棒性，在不同光照下达到最先进性能。
摘要：
准确的息肉分割仍然具有挑战性，这归因于不规则的病变形态、模糊的边界以及异质的成像条件。尽管U-Net变体在局部特征融合方面表现出色，但它们往往缺乏明确的机制来模拟不确定性下分割置信度的动态演化。受基于流的模型的可解释性启发，我们提出了PolypFLow，一种流匹配增强架构，将物理启发的优化动力学注入分割细化过程。与传统的级联网络不同，我们的框架求解一个常微分方程（ODE），通过学习的速度场逐步将粗糙的初始预测与真实掩码对齐。这种基于轨迹的细化提供了两个关键优势：1）可解释的优化：中间流步骤可视化了模型在每个ODE求解器迭代中如何纠正欠分割区域并锐化边界，从而揭开了“黑箱”细化过程的神秘面纱；2）边界感知的鲁棒性：流动力学明确模拟了沿息肉边缘的梯度方向，增强了对低对比度区域和运动伪影的鲁棒性。大量实验结果表明，PolypFLow在不同光照场景下实现了最先进的性能，同时保持了稳定的表现。
英文摘要：
Accurate polyp segmentation remains challenging due to irregular lesion morphologies, ambiguous boundaries, and heterogeneous imaging conditions. While U-Net variants excel at local feature fusion, they often lack explicit mechanisms to model the dynamic evolution of segmentation confidence under uncertainty. Inspired by the interpretable nature of flow-based models, we present \textbf{PolypFLow}, a flow-matching enhanced architecture that injects physics-inspired optimization dynamics into segmentation refinement. Unlike conventional cascaded networks, our framework solves an ordinary differential equation (ODE) to progressively align coarse initial predictions with ground truth masks through learned velocity fields. This trajectory-based refinement offers two key advantages: 1) Interpretable Optimization: Intermediate flow steps visualize how the model corrects under-segmented regions and sharpens boundaries at each ODE-solver iteration, demystifying the ``black-box" refinement process; 2) Boundary-Aware Robustness: The flow dynamics explicitly model gradient directions along polyp edges, enhancing resilience to low-contrast regions and motion artifacts. Numerous experimental results show that PolypFLow achieves a state-of-the-art while maintaining consistent performance in different lighting scenarios.
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。
