Author

Heejong Kim

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Preprint Jul 2026

Unifying Active Learning and Semi-Supervised Learning for Medical Image Segmentation

RegAL is proposed, a unified active semi-supervised framework governed by a shared topology-aware Pareto optimization that couples sample acquisition with unlabeled data utilization and consistently outperforms state-of-the-art AL, SSL, and active semi-supervised baselines across Dice and boundary-distance metrics under extreme annotation scarcity.

Bahram Jafrasteh, Cheng Wan, Heejong Kim et al. · 0 citations