Cross-model Mutual Learning for Exemplar-based Medical Image Segmentation

arXiv:2404.11812v1 Announce Type: new
Abstract: Medical image segmentation typically demands extensive dense annotations for model training, which is both time-consuming and skill-intensive. To mitigate this burden, exemplar-based medical image segmentation methods have been introduced to achieve effective training with only one annotated image. In this paper, we introduce a novel Cross-model Mutual learning framework for Exemplar-based Medical image Segmentation (CMEMS), which leverages two models to mutually excavate implicit information from unlabeled data at multiple granularities. CMEMS can eliminate confirmation bias …

abstract annotations arxiv cs.ai cs.cv image medical novel paper segmentation training type