GMISeg: General Medical Image Segmentation without Re-Training

arXiv:2311.12539v2 Announce Type: replace-cross
Abstract: Although deep learning models have become the main method for medical image segmentation, they often cannot be extended to unknown segmentation tasks involving new anatomical structures, image shapes, or labels. For new segmentation tasks, researchers often have to retrain or fine-tune the model, which is time-consuming and poses a significant obstacle to clinical researchers, who often lack the resources and professional knowledge to train neural networks. Therefore, we proposed a general method that can solve …

abstract arxiv become cs.cv deep learning eess.iv general image labels medical researchers retrain segmentation tasks training type