Self-Ensembling Co-Training Framework for Semi-Supervised COVID-19 CT Segmentation.

ABSTRACT: The coronavirus disease 2019 (COVID-19) has become a severe worldwide health emergency and is spreading at a rapid rate. Segmentation of COVID lesions from computed tomography (CT) scans is of great importance for supervising disease progression and further clinical treatment. As labeling COVID-19 CT scans is labor-intensive and time-consuming, it is essential to develop a segmentation method based on limited labeled data to conduct this task. In this paper, we propose a self-ensembled co-training framework, which is trained by limited labeled data and large-scale unlabeled data, to automatically extract COVID lesions from CT scans. Specifically, to enrich the diversity of unsupervised information, we build a co-training framework consisting of two collaborative models, in which the two mo

SUBMITTER: Li C 

PROVIDER: S-EPMC8904133 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature