AI 中文总结
针对医学图像分类的主动学习部署问题，提出ALDA框架，利用15%-30%试点数据预测最优策略，可降低最多82%标注成本，解决了部署时需选采样策略的关键问题。
AI 中文摘要
主动学习（Active Learning, AL）有望通过减少所需的临床标签数量来降低医学影像项目的成本，但实际部署需在完成全部标注预算前选定采样策略，选错策略反而会增加成本。本文提出Active-Learning Deployment Advisor（ALDA，主动学习部署顾问），这是一种面向部署的、在临床性能约束下选择主动学习方法的框架。在短期试点阶段，ALDA为每个候选策略拟合参数化学习曲线模型，估计该策略是否有望达到所需的临床性能目标，并预测实现该目标所需的专家标注数量。除了绝对标注成本外，ALDA还引入了部署窗口，用于量化该成本估计对临床阈值不确定性的敏感性。最终推荐遵循风险感知规则：在预测成本接近最优的策略中，ALDA偏好部署窗口最窄、对阈值修订最鲁棒的策略。在四个医学影像分类领域的实验表明，ALDA可利用占计划预算15%-30%的试点数据预测出部署最优的方法，与选错策略相比，最多可降低82%的标注成本。ALDA未引入新的采样启发式，而是提供了一个实用的决策层，回答了部署层面的关键问题：需要多少标签才足够？
英文摘要
Active learning (AL) promises to reduce the cost of medical imaging projects by lowering the number of clinical labels required. However, practical deployment requires committing to a sampling strategy before the full annotation budget is spent, and choosing the wrong strategy can increase rather than decrease costs. We propose Active-Learning Deployment Advisor (ALDA), a deployment-oriented framework for AL method selection under clinical performance constraints. Given a short pilot phase, ALDA fits a parametric learning-curve model to each candidate strategy, estimates whether that strategy is expected to reach a required clinical performance target, and predicts the number of expert annotations needed to do so. In addition to absolute annotation cost, ALDA introduces a deployment window that quantifies the sensitivity of this cost estimate to uncertainty in the clinical threshold. The final recommendation follows a risk-aware rule: among strategies with near-optimal predicted cost, ALDA prefers the strategy with the narrowest deployment window, the most robust to threshold revisions. Experiments on four medical imaging classification domains show that ALDA predicts the deployment-optimal method from a pilot of 15-30% of the intended budget and reduces annotation costs by up to 82% compared with a poor strategy choice. Rather than introducing a new sampling heuristic, ALDA provides a practical decision layer that answers a deployment-critical question: how many labels are enough?
CommentsAccepted at EMA4MICCAI Workshop 2026
