Uncertainty-aware Evidential Fusion-based Learning for Semi-supervised Medical Image Segmentation

arXiv:2404.06177v1 Announce Type: new
Abstract: Although the existing uncertainty-based semi-supervised medical segmentation methods have achieved excellent performance, they usually only consider a single uncertainty evaluation, which often fails to solve the problem related to credibility completely. Therefore, based on the framework of evidential deep learning, this paper integrates the evidential predictive results in the cross-region of mixed and original samples to reallocate the confidence degree and uncertainty measure of each voxel, which is realized by emphasizing uncertain information of probability …

abstract arxiv cs.ai cs.cv deep learning evaluation framework fusion image medical paper performance predictive segmentation semi-supervised solve type uncertainty