1 code implementation • 20 Mar 2024 • Yicheng Wu, Xiangde Luo, Zhe Xu, Xiaoqing Guo, Lie Ju, ZongYuan Ge, Wenjun Liao, Jianfei Cai
To address it, the common practice is to gather multiple annotations from different experts, leading to the setting of multi-rater medical image segmentation.
1 code implementation • ICCV 2023 • Wuyang Li, Xiaoqing Guo, Yixuan Yuan
Then, a high-order metric is proposed to match the most significant motif as high-order patterns, serving for motif-guided novel-class learning.
1 code implementation • 16 Jun 2022 • Xiaoqing Guo, Yixuan Yuan
Noisy labels collected with limited annotation cost prevent medical image segmentation algorithms from learning precise semantic correlations.
1 code implementation • CVPR 2022 • Xiaoqing Guo, Jie Liu, Tongliang Liu, Yixuan Yuan
By exploiting computational geometry analysis and properties of segmentation, we design three complementary regularizers, i. e. volume regularization, anchor guidance, convex guarantee, to approximate the true SimT.
1 code implementation • CVPR 2021 • Xiaoqing Guo, Chen Yang, Baopu Li, Yixuan Yuan
Existing self-training based UDA approaches assign pseudo labels for target data and treat them as ground truth labels to fully leverage unlabeled target data for model adaptation.
Ranked #30 on Synthetic-to-Real Translation on SYNTHIA-to-Cityscapes
1 code implementation • 12 Jan 2020 • Xiaoqing Guo, Zhen Chen, Yixuan Yuan
To tackle these issues, we propose a novel complementary network with adaptive receptive filed learning.
1 code implementation • 16 Dec 2019 • Xiaoqing Guo, Chen Yang, Pak Lun Lam, Peter Y. M. Woo, Yixuan Yuan
Automatically segmenting sub-regions of gliomas (necrosis, edema and enhancing tumor) and accurately predicting overall survival (OS) time from multimodal MRI sequences have important clinical significance in diagnosis, prognosis and treatment of gliomas.
no code implementations • 23 Nov 2017 • Siyuan Shan, Wen Yan, Xiaoqing Guo, Eric I-Chao Chang, Yubo Fan, Yan Xu
The contributions of our algorithm are threefold: (1) We transplant traditional image registration algorithms to an end-to-end convolutional neural network framework, while maintaining the unsupervised nature of image registration problems.