Weakly Supervised Segmentation

Weakly supervised segmentation aims to train accurate image segmentation models using limited annotations, such as image-level labels or sparse point annotations, significantly reducing the cost and effort of manual labeling. Current research focuses on leveraging various techniques, including attention mechanisms, multi-task learning, and reinforcement learning, often within the context of specific model architectures like U-Nets and attention-based networks, to improve segmentation accuracy from weak supervision. This field is crucial for advancing medical image analysis, where fully annotated datasets are scarce and expensive to create, enabling applications such as automated diagnosis and surgical planning. The development of robust weakly supervised methods holds significant potential for broader adoption of AI in various domains requiring image segmentation.