no code implementations • 12 Jan 2022 • Zixu Zhuang, Liping Si, Sheng Wang, Kai Xuan, Xi Ouyang, Yiqiang Zhan, Zhong Xue, Lichi Zhang, Dinggang Shen, Weiwu Yao, Qian Wang
Knee osteoarthritis (OA) is the most common osteoarthritis and a leading cause of disability.
1 code implementation • 21 Nov 2021 • Zheren Li, Zhiming Cui, Sheng Wang, Yuji Qi, Xi Ouyang, Qitian Chen, Yuezhi Yang, Zhong Xue, Dinggang Shen, Jie-Zhi Cheng
Specifically, the backbone network is firstly trained with a multi-style and multi-view unsupervised self-learning scheme for the embedding of invariant features to various vendor-styles.
no code implementations • MICCAI Workshop COMPAY 2021 • Xiaodan Xing, Yixin Ma, Lei Jin, Tianyang Sun, Zhong Xue, Feng Shi, Jinsong Wu, Dinggang Shen
The proposed method is featured by a pyramid graph structure and an attention-based multi-instance learning strategy.
no code implementations • 25 May 2020 • Sheng Wang, Jiayu Huo, Xi Ouyang, Jifei Che, Xuhua Ren, Zhong Xue, Qian Wang, Jie-Zhi Cheng
However, the image styles of different vendors are very distinctive, and there may exist domain gap among different vendors that could potentially compromise the universal applicability of one deep learning model.
1 code implementation • 19 May 2020 • Jiayu Huo, Liping Si, Xi Ouyang, Kai Xuan, Weiwu Yao, Zhong Xue, Qian Wang, Dinggang Shen, Lichi Zhang
With dual-consistency checking of the attention in the lesion classification and localization, the two networks can gradually optimize the attention distribution and improve the performance of each other, whereas the training relies on partially labeled data only and follows the semi-supervised manner.
no code implementations • 27 Mar 2020 • Kai Xuan, Liping Si, Lichi Zhang, Zhong Xue, Yining Jiao, Weiwu Yao, Dinggang Shen, Dijia Wu, Qian Wang
In this work, we propose a novel deep-learning-based super-resolution algorithm to generate high-resolution (HR) MR images with small slice spacing from low-resolution (LR) inputs of large slice spacing.
1 code implementation • 10 Mar 2020 • Fei Shan, Yaozong Gao, Jun Wang, Weiya Shi, Nannan Shi, Miaofei Han, Zhong Xue, Dinggang Shen, Yuxin Shi
The performance of the system was evaluated by comparing the automatically segmented infection regions with the manually-delineated ones on 300 chest CT scans of 300 COVID-19 patients.
no code implementations • 30 Jul 2019 • Dongming Wei, Sahar Ahmad, Jiayu Huo, Wen Peng, Yunhao Ge, Zhong Xue, Pew-Thian Yap, Wentao Li, Dinggang Shen, Qian Wang
Then, an unsupervised registration network is used to efficiently align the pre-procedural CT (pCT) with the inpainted iCT (inpCT) image.
no code implementations • 28 Apr 2018 • Xiaohuan Cao, Jianhua Yang, Li Wang, Zhong Xue, Qian Wang, Dinggang Shen
In this paper, we propose to train a non-rigid inter-modality image registration network, which can directly predict the transformation field from the input multimodal images, such as CT and MR images.