Model-Heterogeneous Semi-Supervised Federated Learning for Medical Image Segmentation Background Introduction Medical image segmentation plays a crucial role in clinical diagnosis as it helps doctors identify and analyze diseases. However, this task typically faces challenges such as sensitive data, privacy issues, and expensive annotation costs. W...

Research Background and Motivation Medical image segmentation is of great significance in the image analysis of anatomical structures and lesion areas, as well as in clinical diagnosis. However, existing fully supervised learning methods rely on a large amount of annotated data, and obtaining pixel-level annotated data for medical images is costly ...