Error Localization Network

Error localization networks (ELNs) are auxiliary modules designed to identify and mitigate errors in predictions, particularly within the context of semi-supervised learning and other applications requiring high confidence in model outputs. Current research focuses on developing ELNs using deep learning architectures, such as Swin UNETR, to pinpoint inaccurate predictions in diverse fields, including medical image registration and numerical simulations of partial differential equations. The ability of ELNs to improve the robustness and reliability of predictions holds significant promise for enhancing the accuracy and efficiency of various applications, ranging from surgical planning to computational modeling.