Semi-supervised medical image segmentation network based on mutual learning.
Journal:
Medical physics
Published Date:
Dec 5, 2024
Abstract
BACKGROUND: Semi-supervised learning provides an effective means to address the challenge of insufficient labeled data in medical image segmentation tasks. However, when a semi-supervised segmentation model is overfitted and exhibits cognitive bias, its performance will deteriorate. Errors stemming from cognitive bias can quickly amplify and become difficult to correct during the training process of neural networks, resulting in the continuous accumulation of erroneous knowledge.