Semi Supervised Segmentation

Semi-supervised segmentation aims to improve image segmentation accuracy by leveraging both labeled and unlabeled data, addressing the scarcity of annotated images in many applications. Current research focuses on developing robust methods to generate reliable pseudo-labels from unlabeled data, often employing techniques like consistency regularization, contrastive learning, and teacher-student frameworks within various architectures including CNNs, Transformers, and diffusion models. These advancements are particularly impactful in medical image analysis and remote sensing, where acquiring large labeled datasets is expensive and time-consuming, enabling more efficient and accurate segmentation for diagnosis and resource management.