Single molecule localization microscopy challenge: a biologically inspired benchmark for long-sequence modeling
This work addresses the need for better sequence models for sparse, irregular temporal processes in scientific imaging data, though it is incremental as it primarily introduces a benchmark rather than a new method.
The authors tackled the problem of evaluating state space models on sparse, stochastic temporal processes in biological imaging by introducing the Single Molecule Localization Microscopy Challenge (SMLM-C) benchmark dataset. They found that state space model performance degrades substantially as temporal discontinuity increases, revealing challenges in modeling heavy-tailed blinking dynamics.
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.