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