用生物真实数据测试状态空间模型在稀疏成像中的表现
Single molecule localization microscopy challenge: a biologically inspired benchmark for long-sequence modeling
- 构建了十组生物成像模拟数据，用于评估长序列建模性能
- 发现时间中断越强，模型性能下降越严重，暴露闪烁动态难题
- 适合关注科学成像与稀疏时序建模的研究者参考
状态空间模型（SSMs）在长序列建模中表现优异，且相比Transformer具有更低的内存和计算开销。然而，其评估多局限于语言、音频等合成任务，缺乏对生物成像中稀疏随机时序过程的检验。本文提出单分子定位显微镜挑战（SMLM-C），包含十组dSTORM与DNA-PAINT模态的仿真数据，覆盖不同超参数设置，具备已知真实轨迹。通过控制子集评估，发现随着时间间断增加，模型性能显著下降，揭示其在重尾闪烁动态建模上的根本局限。结果表明，现有序列模型亟需改进以适应真实科学成像中的稀疏不规则时序数据。
原文摘要 · Abstract (English)
State space models (SSMs) have recently achieved strong performance on long sequence modeling tasks while offering improved memory and computational efficiency compared to transformer based architectures. However, their evaluation has been largely limited to synthetic benchmarks and application domains such as language and audio, leaving their behavior on sparse and stochastic temporal processes in biological imaging unexplored. In this work, we introduce the Single Molecule Localization Microscopy Challenge (SMLM-C), a benchmark dataset consisting of ten SMLM simulations spanning dSTORM and DNA-PAINT modalities with varying hyperparameter designed to evaluate state space models on biologically realistic spatiotemporal point process data with known ground truth. Using a controlled subset of these simulations, we evaluate state space models and find that performance degrades substantially as temporal discontinuity increases, revealing fundamental challenges in modeling heavy-tailed blinking dynamics. These results highlight the need for sequence models better suited to sparse, irregular temporal processes encountered in real world scientific imaging data.
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