Diagonal Hierarchical Consistency Learning for Semi-supervised Medical Image Segmentation

arXiv:2311.06031v5 Announce Type: replace
Abstract: Medical image segmentation, which is essential for many clinical applications, has achieved almost human-level performance via data-driven deep learning technologies. Nevertheless, its performance is predicated upon the costly process of manually annotating a vast amount of medical images. To this end, we propose a novel framework for robust semi-supervised medical image segmentation using diagonal hierarchical consistency learning (DiHC-Net). First, it is composed of multiple sub-models with identical multi-scale architecture but with distinct sub-layers, such as …

abstract applications arxiv clinical cs.cv data data-driven deep learning framework hierarchical human image images medical novel performance process segmentation semi-supervised technologies type vast via