no code implementations • 21 Sep 2023 • Jie Jiao, Meiyan Xu, Qingqing Chen, Hefan Zhou, Wangliang Zhou
There is a correlation between adjacent channels of electroencephalogram (EEG), and how to represent this correlation is an issue that is currently being explored.
no code implementations • 26 Oct 2022 • Hongyi Wang, Lanfen Lin, Hongjie Hu, Qingqing Chen, Yinhao Li, Yutaro Iwamoto, Xian-Hua Han, Yen-Wei Chen, Ruofeng Tong
The framework contains two sub-tasks, of which semantic segmentation is the main task and super resolution is an auxiliary task aiding in rebuilding the high frequency information from the LR input.
no code implementations • 23 Mar 2022 • Minghui Wu, Yangdi Xu, Yingying Xu, Guangwei Wu, Qingqing Chen, Hongxiang Lin
In this paper, we propose a stable optimization method for the forward-model-free, LVM-based DIP model for sparse-view CBCT.
no code implementations • 2 Aug 2021 • Yue Zhang, Chengtao Peng, Liying Peng, Huimin Huang, Ruofeng Tong, Lanfen Lin, Jingsong Li, Yen-Wei Chen, Qingqing Chen, Hongjie Hu, Zhiyi Peng
In this work, we propose a novel LiTS method to adequately aggregate multi-phase information and refine uncertain region segmentation.
no code implementations • 27 Feb 2021 • Yingying Xu, Ming Cai, Lanfen Lin, Yue Zhang, Hongjie Hu, Zhiyi Peng, Qiaowei Zhang, Qingqing Chen, Xiongwei Mao, Yutaro Iwamoto, Xian-Hua Han, Yen-Wei Chen, Ruofeng Tong
In this paper, we propose a phase attention residual network (PA-ResSeg) to model multi-phase features for accurate liver tumor segmentation, in which a phase attention (PA) is newly proposed to additionally exploit the images of arterial (ART) phase to facilitate the segmentation of portal venous (PV) phase.