融合注意力网络与变化点检测，精准分析视频中异常扩散行为
AnomalousNet: A Hybrid Approach with Attention U-Nets and Change Point Detection for Accurate Characterization of Anomalous Diffusion in Video Data
- 用注意力U-Net提取轨迹特征，结合变化点检测识别状态跃迁
- 在短时噪声视频数据中仍能准确估计扩散指数和系数
- 适合生物追踪、粒子运动等复杂系统动态分析场景
异常扩散广泛存在于蛋白质在细胞内的运输、动物在复杂栖息地的移动、地下水污染物扩散及合成材料中纳米颗粒运动等系统中。从粒子轨迹准确估算异常扩散指数和扩散系数，是区分亚扩散、超扩散或正常扩散的关键，有助于揭示系统内在动力学机制，识别粒子行为并检测扩散状态变化。然而，传统统计方法在短时、噪声大的视频数据上难以处理不完整且异质的轨迹。本文提出一种数据驱动方法，融合粒子追踪、注意力U-Net架构与变化点检测算法，不仅能高精度推断异常扩散参数，还能在噪声和低时间分辨率下识别状态间的时序转变。该方法在第二届异常扩散（AnDi）挑战赛视频任务的顶级提交中表现优异。
原文摘要 · Abstract (English)
Anomalous diffusion occurs in a wide range of systems, including protein transport within cells, animal movement in complex habitats, pollutant dispersion in groundwater, and nanoparticle motion in synthetic materials. Accurately estimating the anomalous diffusion exponent and the diffusion coefficient from the particle trajectories is essential to distinguish between sub-diffusive, super-diffusive, or normal diffusion regimes. These estimates provide a deeper insight into the underlying dynamics of the system, facilitating the identification of particle behaviors and the detection of changes in diffusion states. However, analyzing short and noisy video data, which often yield incomplete and heterogeneous trajectories, poses a significant challenge for traditional statistical approaches. We introduce a data-driven method that integrates particle tracking, an attention U-Net architecture, and a change-point detection algorithm to address these issues. This approach not only infers the anomalous diffusion parameters with high accuracy but also identifies temporal transitions between different states, even in the presence of noise and limited temporal resolution. Our methodology demonstrated strong performance in the 2nd Anomalous Diffusion (AnDi) Challenge benchmark within the top submissions for video tasks.
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