Achieving diversified and personalized results in multi-rater medical image segmentation through a two-stage framework.
The core message of this paper is to propose a prompt-based framework, called PU-Net, that can effectively handle ambiguous multi-rater annotations for medical image segmentation tasks. PU-Net utilizes rater-aware prompts to learn the uncertainty among multiple raters and significantly reduces the computation cost required for fine-tuning the model on different dataset domains.