Inter- and intra-uncertainty based feature aggregation model for semi-supervised histopathology image segmentation

arXiv:2403.12767v1 Announce Type: new
Abstract: Acquiring pixel-level annotations is often limited in applications such as histology studies that require domain expertise. Various semi-supervised learning approaches have been developed to work with limited ground truth annotations, such as the popular teacher-student models. However, hierarchical prediction uncertainty within the student model (intra-uncertainty) and image prediction uncertainty (inter-uncertainty) have not been fully utilized by existing methods. To address these issues, we first propose a novel inter- and intra-uncertainty regularization method to measure and …

abstract aggregation annotations applications arxiv cs.cv domain expertise feature hierarchical however image pixel popular prediction segmentation semi-supervised semi-supervised learning studies supervised learning truth type uncertainty work