Semi Supervised Medical Image Segmentation

Semi-supervised medical image segmentation aims to improve the accuracy of medical image analysis by leveraging both limited labeled and abundant unlabeled data. Current research focuses on developing novel consistency regularization techniques, often employing teacher-student models, contrastive learning, and advanced architectures like transformers and UNets, to effectively utilize unlabeled data and mitigate the impact of noisy pseudo-labels. These advancements are significant because they address the critical shortage of annotated medical images, potentially accelerating the development and deployment of AI-driven diagnostic and treatment planning tools. The resulting models promise improved efficiency and accuracy in medical image analysis, ultimately benefiting patient care.