no code implementations • 5 Dec 2021 • Yichi Zhang, Rushi Jiao, Qingcheng Liao, Dongyang Li, Jicong Zhang
In this paper, we propose a novel uncertainty-guided mutual consistency learning framework to effectively exploit unlabeled data by integrating intra-task consistency learning from up-to-date predictions for self-ensembling and cross-task consistency learning from task-level regularization to exploit geometric shape information.
no code implementations • 31 Dec 2020 • Yichi Zhang, Qingcheng Liao, Lin Yuan, He Zhu, Jiezhen Xing, Jicong Zhang
In this paper, we propose a novel relation-driven collaborative learning model to exploit shared knowledge from non-COVID lesions for annotation-efficient COVID-19 CT lung infection segmentation.
no code implementations • 13 Oct 2020 • Yichi Zhang, Qingcheng Liao, Le Ding, Jicong Zhang
Despite these works lead to improvements on a variety of segmentation tasks, to the best of our knowledge, there has not previously been a large-scale empirical comparison of these methods.