DM4ISL: high-fidelity and uncertainty-aware in silico labeling of fluorescence images via diffusion models

DM4ISL, a diffusion-model approach for in silico labeling, delivers higher structural fidelity than state-of-the-art methods across six organelles, while producing uncertainty maps from denoising steps.

• Why it matters: Fluorescence imaging mixes biological signal with noise, and existing in silico labeling can yield blurry or structurally inaccurate predictions that also lack reliable measures of when outputs are wrong.
• What they did: DM4ISL uses step-wise self-ensemble averaging across diffusion iterations to marginalize stochastic fluctuations, and it benchmarks performance with both pixel-based and application-specific metrics.
• The result: Standard deviation across intermediate denoising steps forms localized uncertainty maps that detect erroneous predictions during inference, enabling high-fidelity, reliability-aware discovery for cell biology.

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