动态融合多种策略，让医疗影像标注更省力高效
WaveFuse-AL: Cyclical and Performance-Adaptive Multi-Strategy Active Learning for Medical Images
- 按周期和模型表现自动调节不同选样策略权重
- 在三个医学数据集上均显著优于单一或轮换策略
- 适合标注资源紧张的医疗AI研发团队使用
主动学习通过有选择地标注最具有信息量的样本，降低医疗影像标注成本。然而，单一选样策略在主动学习周期的不同阶段表现不稳定。我们提出一种新型框架WaveFuse-AL，它在学习过程中自适应融合四种成熟选样策略——BALD、BADGE、熵值和CoreSet。该方法结合正弦周期性时间先验与性能驱动的动态调整机制，实时优化各策略的重要性。我们在三个医学影像基准上评估：APTOS-2019（多分类）、RSNA Pneumonia Detection（二分类）和ISIC-2018（皮肤病变分割）。实验表明，WaveFuse-AL持续优于单策略和交替策略基线，在十二项指标中的十项实现统计显著提升，同时最大化有限标注预算的利用效率。
原文摘要 · Abstract (English)
Active learning reduces annotation costs in medical imaging by strategically selecting the most informative samples for labeling. However, individual acquisition strategies often exhibit inconsistent behavior across different stages of the active learning cycle. We propose Cyclical and Performance-Adaptive Multi-Strategy Active Learning (WaveFuse-AL), a novel framework that adaptively fuses multiple established acquisition strategies-BALD, BADGE, Entropy, and CoreSet throughout the learning process. WaveFuse-AL integrates cyclical (sinusoidal) temporal priors with performance-driven adaptation to dynamically adjust strategy importance over time. We evaluate WaveFuse-AL on three medical imaging benchmarks: APTOS-2019 (multi-class classification), RSNA Pneumonia Detection (binary classification), and ISIC-2018 (skin lesion segmentation). Experimental results demonstrate that WaveFuse-AL consistently outperforms both single-strategy and alternating-strategy baselines, achieving statistically significant performance improvements (on ten out of twelve metric measurements) while maximizing the utility of limited annotation budgets.
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