One-Prompt to Segment All Medical Images

arXiv:2305.10300v4 Announce Type: replace-cross
Abstract: Large foundation models, known for their strong zero-shot generalization, have excelled in visual and language applications. However, applying them to medical image segmentation, a domain with diverse imaging types and target labels, remains an open challenge. Current approaches, such as adapting interactive segmentation models like Segment Anything Model (SAM), require user prompts for each sample during inference. Alternatively, transfer learning methods like few/one-shot models demand labeled samples, leading to high costs. This paper introduces a …

arxiv cs.cv eess.iv images medical prompt segment type