A transformation uncertainty and multi-scale contrastive learning-based semi-supervised segmentation method for oral cavity-derived cancer

Our semi-supervised method, integrating transformation uncertainty, multi-scale contrastive learning, and a boundary-aware enhanced U-Net, effectively addresses data scarcity and improves segmentation accuracy. This approach reduces the dependency on large labeled datasets, promoting the application of AI in OSCC detection and improving the efficiency and accuracy of clinical diagnoses for OSCC.