无需标注数据，实时解析电子显微图像，提升自动化实验效率
Unsupervised Reward-Driven Image Segmentation in Automated Scanning Transmission Electron Microscopy Experiments
- 用奖励驱动优化实现无监督图像分割，避免人工标注依赖
- 在动态高通量实验中实现毫秒级实时分析，性能稳定可靠
- 结果可解释，适合科研人员调试模型与拓展到多种微观成像任务
扫描透射电子显微镜（STEM）自动化实验需快速图像分割以优化数据呈现，支持人工解读、决策、定点光谱分析和原子操控。当前分割多依赖监督学习，需人工标注数据，且易受分辨率、采样或束斑形状变化导致的分布外漂移影响。本文实施并评估了一种近期提出的奖励驱动优化流程，用于STEM中的实时图像分析。该无监督方法不依赖人工标签，具有强鲁棒性且完全可解释。解释性反馈可帮助研究人员验证决策过程，并通过在奖励函数的帕累托前沿上选择位置来调整模型。我们验证了该方法的时间效率与有效性，证明其适用于高通量、动态的自动化STEM实验。奖励驱动框架能构建可解释、稳健的分析流程，可推广至电子显微镜、扫描探针显微镜及化学成像等广泛图像分析任务。
原文摘要 · Abstract (English)
Automated experiments in scanning transmission electron microscopy (STEM) require rapid image segmentation to optimize data representation for human interpretation, decision-making, site-selective spectroscopies, and atomic manipulation. Currently, segmentation tasks are typically performed using supervised machine learning methods, which require human-labeled data and are sensitive to out-of-distribution drift effects caused by changes in resolution, sampling, or beam shape. Here, we operationalize and benchmark a recently proposed reward-driven optimization workflow for on-the fly image analysis in STEM. This unsupervised approach is much more robust, as it does not rely on human labels and is fully explainable. The explanatory feedback can help the human to verify the decision making and potentially tune the model by selecting the position along the Pareto frontier of reward functions. We establish the timing and effectiveness of this method, demonstrating its capability for real-time performance in high-throughput and dynamic automated STEM experiments. The reward driven approach allows to construct explainable robust analysis workflows and can be generalized to a broad range of image analysis tasks in electron and scanning probe microscopy and chemical imaging.
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