Shot Medical Image Segmentation

Few-shot medical image segmentation aims to accurately segment medical images using only a limited number of labeled examples, addressing the scarcity of annotated data in medical imaging. Current research focuses on improving model generalization across different imaging modalities and institutions, employing techniques like prototype-based learning, attention mechanisms (including cross-attention and large kernel attention), and the adaptation of foundation models such as SAM. These advancements are crucial for efficient and robust medical image analysis, potentially accelerating clinical workflows and improving diagnostic accuracy in resource-constrained settings.