
Achieving diversified and personalized results in multi-rater medical image segmentation through a two-stage framework.


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diversified-and-personalized-multi-rater-medical-image-segmentation-study


Diversified and Personalized Multi-rater Medical Image Segmentation Study


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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.


leveraging-prompt-learning-to-handle-ambiguous-multi-rater-annotations-in-medical-image-segmentation


Leveraging Prompt Learning to Handle Ambiguous Multi-Rater Annotations in Medical Image Segmentation
