2024
Breaking the Barrier: Selective Uncertainty-Based Active Learning for Medical Image Segmentation
ICASSP 2024accepted
Active learning (AL) has found wide applications in medical image segmentation, aiming to alleviate the annotation workload and enhance performance. Conventional uncertainty-based AL methods, such as entropy and Bayesian, often rely on an aggregate of all pixel-level metrics. However, in imbalanced…