Semi-supervised segmentation of medical images focused on the pixels with unreliable predictions

Pseudo-labeling is a well-studied approach in semi-supervised learning. However, unreliable or potentially incorrect pseudo-labels can accumulate training errors during iterative self-training steps, leading to unstable performance. Addressing this challenge typically involves either discarding unreliable pseudo-labels, resulting in the loss of important data, or attempting to refine them, risking the possibility of worsening the pseudo-labels …