How Many Labels Are Enough? ALDA: Active Learning Deployment Advisor for Medical Image Classification

2026-08-04

AI Summary

This work addresses the challenge of selecting an appropriate active learning strategy in medical image classification, where suboptimal choices can significantly increase annotation costs and must be made before exhausting limited labeling budgets. To this end, the authors propose the ALDA framework, which employs a brief pilot phase—using only 15–30% of the total budget—to fit learning curves of candidate strategies and predict the annotation effort required to meet clinical performance targets. ALDA introduces a “deployment window” to quantify sensitivity to uncertainty in clinical thresholds and integrates risk-aware decision rules to recommend the optimal strategy. By reframing strategy selection as a deployment-oriented decision problem, ALDA jointly optimizes annotation cost and robustness. Experiments demonstrate that ALDA accurately identifies the best-performing strategy, reducing annotation costs by up to 82% compared to poor alternatives.

Abstract

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?

Problem

• active learning
• medical image classification
• annotation cost
• clinical performance
• deployment strategy

Innovation

• Active Learning
• Medical Image Classification
• Learning Curve Modeling
• Deployment Window
• Annotation Budget Optimization