no code implementations • 23 Mar 2024 • Ruige Zong, Tao Wang, Chunwang Li, Xinlin Zhang, Yuanbin Chen, Longxuan Zhao, Qixuan Li, Qinquan Gao, Dezhi Kang, Fuxin Lin, Tong Tong
To alleviate this problem, we propose a quantitative statistical framework for FCCM, comprising an efficient annotation module, an FCCM lesion segmentation module, and an FCCM lesion quantitative statistics module.
1 code implementation • 13 Dec 2023 • Yuanbo Zhou, Yuyang Xue, Jiang Bi, Wenlin He, Xinlin Zhang, Jiajun Zhang, Wei Deng, Ruofeng Nie, Junlin Lan, Qinquan Gao, Tong Tong
Real-world stereo image super-resolution has a significant influence on enhancing the performance of computer vision systems.
1 code implementation • 17 Nov 2023 • Tao Wang, Yuanbin Chen, Xinlin Zhang, Yuanbo Zhou, Junlin Lan, Bizhe Bai, Tao Tan, Min Du, Qinquan Gao, Tong Tong
Inspired by semi-supervised algorithms that use both labeled and unlabeled data for training, we propose the PLGDF framework, which builds upon the mean teacher network for segmenting medical images with less annotation.
1 code implementation • 31 Aug 2023 • Yuanbin Chen, Tao Wang, Hui Tang, Longxuan Zhao, Ruige Zong, Shun Chen, Tao Tan, Xinlin Zhang, Tong Tong
In this paper, we present a novel semi-supervised learning method, Dual-Decoder Consistency via Pseudo-Labels Guided Data Augmentation (DCPA), for medical image segmentation.
1 code implementation • 29 Jun 2023 • Tao Wang, Xinlin Zhang, Yuanbo Zhou, Junlin Lan, Tao Tan, Min Du, Qinquan Gao, Tong Tong
To address this limitation, we propose an AL-based method that can be simultaneously applied to 2D medical image classification, segmentation, and 3D medical image segmentation tasks.
no code implementations • 24 Nov 2022 • Yihui Huang, Zi Wang, Xinlin Zhang, Jian Cao, Zhangren Tu, Meijin Lin, Di Guo, Xiaobo Qu
Undersampling can accelerate the signal acquisition but at the cost of bringing in artifacts.
no code implementations • 28 Mar 2022 • Chen Qian, Zi Wang, Xinlin Zhang, Boxuan Shi, Boyu Jiang, Ran Tao, Jing Li, Yuwei Ge, Taishan Kang, Jianzhong Lin, Di Guo, Xiaobo Qu
Conclusion: The explicit phase model PAIR with complementary priors has a good performance on challenging reconstructions under inter-shot motions between shots and a low signal-to-noise ratio.
no code implementations • 24 Jul 2021 • Xinlin Zhang, Hengfa Lu, Di Guo, Zongying Lai, Huihui Ye, Xi Peng, Bo Zhao, Xiaobo Qu
The combination of the sparse sampling and the low-rank structured matrix reconstruction has shown promising performance, enabling a significant reduction of the magnetic resonance imaging data acquisition time.
no code implementations • 24 Sep 2019 • Tieyuan Lu, Xinlin Zhang, Yihui Huang, Yonggui Yang, Gang Guo, Lijun Bao, Feng Huang, Di Guo, Xiaobo Qu
Magnetic resonance imaging has been widely applied in clinical diagnosis, however, is limited by its long data acquisition time.
no code implementations • 17 Sep 2019 • Xinlin Zhang, Hengfa Lu, Di Guo, Lijun Bao, Feng Huang, Qin Xu, Xiaobo Qu
The pFISTA, a simple and efficient algorithm for sparse reconstruction, has been successfully extended to parallel imaging.