ProMISe: Promptable Medical Image Segmentation using SAM

arXiv:2403.04164v1 Announce Type: new
Abstract: With the proposal of the Segment Anything Model (SAM), fine-tuning SAM for medical image segmentation (MIS) has become popular. However, due to the large size of the SAM model and the significant domain gap between natural and medical images, fine-tuning-based strategies are costly with potential risk of instability, feature damage and catastrophic forgetting. Furthermore, some methods of transferring SAM to a domain-specific MIS through fine-tuning strategies disable the model's prompting capability, severely limiting its utilization …

abstract arxiv become cs.ai cs.cv domain fine-tuning gap however image images medical natural popular risk sam segment segment anything segment anything model segmentation strategies type