为细胞追踪算法添加不确定性评估，让系统学会说‘我也不确定’。
How To Make Your Cell Tracker Say "I dunno!"
- 将追踪问题视为贝叶斯推断与分类任务，构建统一的不确定性量化框架。
- 在多个追踪算法上验证，不确定性估计结果准确且与真实误差匹配。
- 适用于各类帧间追踪方法，尤其适合高通量显微成像中的可靠分析。
细胞追踪是活细胞显微镜中的关键计算任务，但高通量成像产生的数据量远超人工处理能力，因此需要具备不确定性感知的数据分析工具。本文提出并基准测试了多种基于线性分配的细胞追踪不确定性量化方法。方法灵感来自统计学与机器学习，从贝叶斯推断和分类两个视角建模追踪问题。所提方法具有框架式特性，可为任意帧间追踪算法附加不确定性评估能力。我们将其应用于包括最近提出的基于Transformer的追踪器在内的多种现有算法，并实证表明其生成的不确定性估计既有效又校准良好。
原文摘要 · Abstract (English)
Cell tracking is a key computational task in live-cell microscopy, but fully automated analysis of high-throughput imaging requires reliable and, thus, uncertainty-aware data analysis tools, as the amount of data recorded within a single experiment exceeds what humans are able to overlook. We here propose and benchmark various methods to reason about and quantify uncertainty in linear assignment-based cell tracking algorithms. Our methods take inspiration from statistics and machine learning, leveraging two perspectives on the cell tracking problem explored throughout this work: Considering it as a Bayesian inference problem and as a classification problem. Our methods admit a framework-like character in that they equip any frame-to-frame tracking method with uncertainty quantification. We demonstrate this by applying it to various existing tracking algorithms including the recently presented Transformer-based trackers. We demonstrate empirically that our methods yield useful and well-calibrated tracking uncertainties.
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