终于超越随机基线：三维生物医学图像主动学习的简易高效解决方案

摘要

主动学习（AL）在3D生物医学图像分割领域具有显著降低标注成本的潜力，因为专家对体数据的标注既耗时又昂贵。然而，现有AL方法始终无法稳定超越针对3D数据优化的改进型随机采样基线，导致该领域缺乏可靠解决方案。我们提出类分层调度幂预测熵（ClaSP PE）这一简单高效的查询策略，解决了标准基于不确定性的AL方法的两大关键局限：类别不平衡和早期选择冗余。ClaSP PE通过类分层查询确保对低代表性结构的覆盖，结合对数尺度幂噪声与衰减调度机制，在AL早期阶段强制实现查询多样性，后期则促进针对性挖掘。在使用综合nnActive基准测试中四个3D生物医学数据集进行的24组实验评估表明，ClaSP PE是唯一能在分割质量上稳定超越改进型随机基线的方法（具有统计学显著增益），同时保持标注效率。此外，我们通过在四个未见数据集上无需人工适配的测试，模拟了实际应用场景：所有实验参数均依据预设指南设置。结果证实ClaSP PE能够稳健地泛化至新任务，无需针对特定数据集调参。在nnActive框架内，我们提供了有力证据表明：在接近实际生产的场景下，AL方法可以在性能和标注效率两方面持续超越适用于3D分割的随机基线。我们的开源实现和清晰部署指南使其具备即用性。代码详见：https://github.com/MIC-DKFZ/nnActive。

English

Active learning (AL) has the potential to drastically reduce annotation costs in 3D biomedical image segmentation, where expert labeling of volumetric data is both time-consuming and expensive. Yet, existing AL methods are unable to consistently outperform improved random sampling baselines adapted to 3D data, leaving the field without a reliable solution. We introduce Class-stratified Scheduled Power Predictive Entropy (ClaSP PE), a simple and effective query strategy that addresses two key limitations of standard uncertainty-based AL methods: class imbalance and redundancy in early selections. ClaSP PE combines class-stratified querying to ensure coverage of underrepresented structures and log-scale power noising with a decaying schedule to enforce query diversity in early-stage AL and encourage exploitation later. In our evaluation on 24 experimental settings using four 3D biomedical datasets within the comprehensive nnActive benchmark, ClaSP PE is the only method that generally outperforms improved random baselines in terms of both segmentation quality with statistically significant gains, whilst remaining annotation efficient. Furthermore, we explicitly simulate the real-world application by testing our method on four previously unseen datasets without manual adaptation, where all experiment parameters are set according to predefined guidelines. The results confirm that ClaSP PE robustly generalizes to novel tasks without requiring dataset-specific tuning. Within the nnActive framework, we present compelling evidence that an AL method can consistently outperform random baselines adapted to 3D segmentation, in terms of both performance and annotation efficiency in a realistic, close-to-production scenario. Our open-source implementation and clear deployment guidelines make it readily applicable in practice. Code is at https://github.com/MIC-DKFZ/nnActive.

终于超越随机基线：三维生物医学图像主动学习的简易高效解决方案

Finally Outshining the Random Baseline: A Simple and Effective Solution for Active Learning in 3D Biomedical Imaging

摘要

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