Overview
Active learning selects the most informative cases for annotation to reduce labeling burden. It accelerates dataset curation and improves model performance with fewer labels. This approach is valuable for costly expert annotations.
Selection Strategies
Uncertainty sampling and diversity based selection identify high value cases. Iterative annotation cycles refine models and guide further selection. Human in the loop workflows optimize efficiency.
Clinical Impact
Active learning reduces time and cost for building clinical grade datasets. It enables rapid adaptation to new tasks and modalities. Collaboration between clinicians and data scientists is essential.
Limitations
Selection bias and annotation variability can affect outcomes. Clear stopping criteria and validation strategies ensure robust models. Documentation of annotation provenance supports reproducibility.