Bio-Inspired Multi-Objective Batch Active Learning for Skin Lesion Classification

Deep learning models for medical image classification achieve high accuracy but rely on large, costly labeled datasets. Active Learning (AL) mitigates this by iteratively selecting informative samples under a fixed labeling budget. However, batch-mode AL often struggles with redundancy when relying solely on scalar uncertainty scores. In this work, we formulate batch-mode AL for skin lesion classification as a Multi-Objective Optimization (MOO) problem, explicitly balancing informativeness, coverage, and diversity. We employ bio-inspired optimizers, NSGA-II with set-based encoding and MOPSO with continuous scoring vectors, to search for Pareto-optimal batches. We evaluated our MOO approaches on the HAM10000 dataset, where they were able to outperform established baselines in sample efficiency, with MOPSO attaining the highest Area Under the Learning Curve (AULC). Furthermore, Pareto-front analyses provide interpretable insights into the trade-offs between the competing objectives. This work demonstrates that evolutionary multi-objective algorithms offer a superior, transparent approach to resolving conflicting acquisition criteria in deep Active Learning.